Optimized Airborne Oil Spill Remote Sensing: POSEIDON, the Quantitative Approach

2017 ◽  
Vol 2017 (1) ◽  
pp. 1594-1611
Author(s):  
Guilherme Pinho ◽  
Alessandro Vagata ◽  
Theo Hengstermann
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Oil Spill ◽  
Oil Spills ◽  
Response Strategy ◽  
Geographical Information ◽  
Web Based ◽  
Incident Command ◽  
Automated Processing ◽  
The Right

ABSTRACT Aerial surveillance is becoming a foundation on the overall oil spill response strategy due to the ability to plan and tactically position response resources in the optimal areas of oil migration. It takes a complete multitasking approach to effectively respond to oil spills. While much of the regulatory focus to date has been on the resources on the sea - vessels, skimmers, dispersants - the reality is that they are only one of the components and not necessarily the most important in combating oil spills. It is imperative to determine the location of oil that is most recoverable, and give quantitative information - thickness, volume, area, classification - whether day or night. Having the right information at the right time optimizes dramatically the use of all the response resources. And assess the effectiveness of the response and make an accurate natural resources damage assessment is critical and requires as well quantitative and timely information. In the past the main effort has been directed towards developing airborne sensors with enhanced spill monitoring capability. Recently, more and more attention has been paid to the automated processing of oil spill data acquired by integrated airborne sensor platforms. Automated processing and real time relay of immediately usable information to the Incident Command Center is critical during all phases of response. This paper focuses on advanced data processing and presents ways of improving the usability of airborne multi-sensor oil spill monitoring systems. In this context, is given an overview of currently existing oil spill remote sensing technology like infrared/ultraviolet line scanners, microwave radiometers, laser fluorosensors and radar system. The paper presents POSEIDON, a system for network-based real-time data acquisition, analysis and fusion of multi-sensor data. Also, a method for the distribution of oil spill data and related data products using web-based geographical information systems is described; automated generation of thematic maps of the oil spill scene along with their real-time web-based distribution is becoming more important in marine incident management.

scholarly journals Next-Generation Smart Response Web (NG-SRW): An Operational Spatial Decision Support System for Maritime Oil Spill Emergency Response in the Gulf of Finland (Baltic Sea)

2021 ◽  
Vol 13 (12) ◽  
pp. 6585
Author(s):  
Mihhail Fetissov ◽  
Robert Aps ◽  
Floris Goerlandt ◽  
Holger Jänes ◽  
Jonne Kotta ◽  
...  
Keyword(s):  
Baltic Sea ◽  
Oil Spill ◽  
Situational Awareness ◽  
Oil Spills ◽  
Relevant Information ◽  
Response Strategy ◽  
Spatial Decision Support System ◽  
Next Generation ◽  
Oil Spill Response ◽  
The Baltic

The Baltic Sea is a unique and sensitive brackish-water ecosystem vulnerable to damage from shipping activities. Despite high levels of maritime safety in the area, there is a continued risk of oil spills and associated harmful environmental impacts. Achieving common situational awareness between oil spill response decision makers and other actors, such as merchant vessel and Vessel Traffic Service center operators, is an important step to minimizing detrimental effects. This paper presents the Next-Generation Smart Response Web (NG-SRW), a web-based application to aid decision making concerning oil spill response. This tool aims to provide, dynamically and interactively, relevant information on oil spills. By integrating the analysis and visualization of dynamic spill features with the sensitivity of environmental elements and value of human uses, the benefits of potential response actions can be compared, helping to develop an appropriate response strategy. The oil spill process simulation enables the response authorities to judge better the complexity and dynamic behavior of the systems and processes behind the potential environmental impact assessment and thereby better control the oil combat action.

Remote Sensing in Oil Spill Handling in Offshore North West Java

2021 ◽  
Author(s):  
Audra Ligafinza ◽  
Farasdaq Muchibbus Sajjad ◽  
Mohammad Abdul Jabbar ◽  
Anggia Fatmawati ◽  
Alvin Derry Wirawan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Oil Spill ◽  
Environmental Damage ◽  
Satellite Imaging ◽  
North West ◽  
Novel Technologies ◽  
Oil Boom ◽  
Remediation Strategies ◽  
Restoration Strategies

Abstract During the blowout event, it is critical to track the oil spill to minimize environmental damage and optimize restoration cost. In this paper, we deliver our success story in handling oil spill from recent experiences. We utilize remote sensing technologies to establish our analysis and plan the remediation strategies. We also comprehensively discuss the techniques to analyze big data from the satellites, to utilize the downloaded data for forecasting, and to align the satellite information with restoration strategies. PHE relies on its principle to maintain minimum damage and ensures safety by dividing the steps into several aspects of monitoring, response (offshore and onshore), shoreline management and waste management. PHE utilizes latest development in survey by using satellite imaging, survey boat, chopper and UAV drone. Spill containment is done using several layers of oil boom to recover oil spill, complemented with skimmers and storage tanks. PHE encourages shoreline remediation using nets and manual recovery for capturing oil sludge. Using this combination of technologies, PHE is able to model and anticipate oil spill movement from the source up until the farthest shoreline. This enables real time monitoring and handling, therefore minimum environmental damage is ensured. PHE also employs prudent engineering design based on real time field condition in order to ensure the equipment are highly suited for the condition, as well as ensuring good supply chain of the material availability. This publication addresses the first offshore blowout mitigation and handling in Indonesia that uses novel technologies such as static oil boom, satellite imaging and integrated effort in handling shoreline damage. It is hoped that the experience can be replicated for other offshore operating contractors in Indonesia in designing blowout remediation.

scholarly journals Oil spill remote monitoring by using remotely piloted aircraft

2019 ◽  
Vol 91 (4) ◽  
pp. 648-653
Author(s):  
Aleksandrs Urbahs ◽  
Vladislavs Zavtkevics
Keyword(s):  
Remote Sensing ◽  
Oil Spill ◽  
Multispectral Imaging ◽  
Oil Spills ◽  
Oil Pollution ◽  
Thickness Measurement ◽  
Quality Data ◽  
Atmospheric Conditions ◽  
Content Type ◽  
Remotely Piloted Aircraft

Purpose This paper aims to analyze the application of remotely piloted aircraft (RPA) for remote oil spill sensing. Design/methodology/approach This paper is an analysis of RPA strong points. Findings To increase the accuracy and eliminate potentially false contamination detection, which can be caused by external factors, an oil thickness measurement algorithm is used with the help of the multispectral imaging that provides high accuracy and is versatile for any areas of water and various meteorological and atmospheric conditions. Research limitations/implications SWOT analysis of implementation of RPA for remote sensing of oil spills. Practical implications The use of RPA will improve the remote sensing of oil spills. Social implications The concept of oil spills monitoring needs to be developed for quality data collection, oil pollution control and emergency response. Originality/value The research covers the development of a method and design of a device intended for taking samples and determining the presence of oil contamination in an aquatorium area; the procedure includes taking a sample from the water surface, preparing it for transportation and delivering the sample to a designated location by using the RPA. The objective is to carry out the analysis of remote oil spill sensing using RPA. The RPA provides a reliable sensing of oil pollution with significant advantages over other existing methods. The objective is to analyze the use of RPA employing all of their strong points. In this paper, technical aspects of sensors are analyzed, as well as their advantages and limitations.

ESTIMATING MORTALITY OF SEABIRDS FROM OIL SPILLS

1987 ◽  
Vol 1987 (1) ◽  
pp. 547-551 ◽  
Author(s):  
R. Glenn Ford ◽  
Gary W. Page ◽  
Harry R. Carter
Keyword(s):  
Real Time ◽  
Emergency Response ◽  
Oil Spill ◽  
Potential Application ◽  
Damage Assessment ◽  
Oil Spills ◽  
Central California ◽  
Marine Oil ◽  
Large Numbers ◽  
Using Data

ABSTRACT From an aesthetic and damage assessment standpoint, the loss of seabirds may be one of the more important results of a marine oil spill. Assessment of the actual numbers of seabirds killed is difficult because the bodies of dead or incapacitated seabirds are often never found or recorded. We present a computer methodology that estimates the number of birds that come in contact with an oil spill and partitions these birds among four possible fates: (1) swimming or flying ashore under their own power; (2) carried out to sea by winds and currents; (3) carried inshore, but lost before being beached; and (4) beached by winds and currents. Beached birds are further divided into those that are recovered and those that are not. The accuracy of the methodology is examined using data for two recent spills in central California, each of which resulted in the beachings of large numbers of birds. The methodology also has potential application to real-time emergency response by predicting when and where the greatest numbers of bird beachings will occur.

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 CONTINGENCY PLANNING FOR MAJOR TERMINALS AND SENSITIVE AREAS

1990 ◽  
Vol 30 (1) ◽  
pp. 413
Author(s):  
C. Jones ◽  
J. P. Hartley
Keyword(s):  
Oil Spill ◽  
Staff Training ◽  
Oil Spills ◽  
Primary Objective ◽  
Lessons Learned ◽  
Response Strategy ◽  
Plant Design ◽  
Technological Advances ◽  
Resource Levels ◽  
Spill Control

The BP Exploration approach to oil spill control can be summed up as prevention and preparedness. In all cases our primary objective is to prevent oil spills occurring. However despite careful attention to plant design, staff training, auditing etc., oil may sometimes be spilled.For any operation, effective oil spill ontingency planning depends on having a sound understanding of the local ecological and environmental sensitivities, physical conditions and the nature, size and risks of potential spills. This information allows the definition of response strategy and appropriate resource levels (equipment and personnel). However the mere provision of resources is insufficient; equipment maintenance, staff training, oil spill exercises (planned and unannounced), agreement of responsibilities with external authorities and periodic reviews are regarded as essential to ensure adequacy of response.The implementation of these principles is demonstrated using the development and continued evolution of the oil spill plan for Sullom Voe, a major North Sea oil terminal handling ca 1 million barrels of crude per day. Changes have been made to the plan to take account of technological advances and the lessons learned from actual spills in Sullom Voe, Port Valdez and elsewhere.Oil spill contingency arrangements for onshore and nearshore exploration drilling are also considered, illustrated with recent English (on and offshore Wytch Farm) and Scottish west coast examples. The principles adopted for spill planning at oil terminals have been found to apply equally to E & P operations in sensitive areas.The paper concludes with a brief comparison of the relative costs of efforts to prevent spills with the costs of spill cleanup and damages.

scholarly journals Space-time clustering characteristics of dengue based on ecological, socio-economic and demographic factors in northern Sri Lanka

2015 ◽  
Vol 10 (2) ◽  
Author(s):  
Sumiko Anno ◽  
Keiji Imaoka ◽  
Takeo Tadono ◽  
Tamotsu Igarashi ◽  
Subramaniam Sivaganesh ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Sri Lanka ◽  
Land Cover ◽  
Demographic Factors ◽  
Space Time ◽  
Geographical Information ◽  
Temporal Association ◽  
Stable Model ◽  
The Right ◽  
Socioeconomic And Demographic Factors

The aim of the present study was to identify geographical areas and time periods of potential clusters of dengue cases based on ecological, socio-economic and demographic factors in northern Sri Lanka from January 2010 to December 2013. Remote sensing (RS) was used to develop an index comprising rainfall, humidity and temperature data. Remote sensing data gathered by the AVNIR-2 instrument onboard the ALOS satellite were used to detect urbanisation, and a digital land cover map was used to extract land cover information. Other data on relevant factors and dengue outbreaks were collected through institutions and extant databases. The analysed RS data and databases were integrated into a geographical information system (GIS) enabling space-time clustering analysis. Our results indicate that increases in the number of combinations of ecological, socio-economic and demographic factors that are present or above the average contribute to significantly high rates of space-time dengue clusters. The spatio-temporal association that consolidates the two kinds of associations into one can ensure a more stable model for forecasting. An integrated spatiotemporal prediction model at a smaller level using ecological, socioeconomic and demographic factors could lead to substantial improvements in dengue control and prevention by allocating the right resources to the appropriate places at the right time.

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