scholarly journals Oil Spill Detection Using Fluorometric Sensors: Laboratory Validation and Implementation to a FerryBox and a Moored SmartBuoy

2021 ◽  
Vol 8 ◽  
Author(s):  
Siim Pärt ◽  
Harri Kankaanpää ◽  
Jan-Victor Björkqvist ◽  
Rivo Uiboupin
Keyword(s):  
Real Time ◽  
Oil Spill ◽  
Oil Spills ◽  
Autonomous Systems ◽  
Field Tests ◽  
Primary Source ◽  
Visual Surveillance ◽  
Fluorescence Signal ◽  
Time Data ◽  
Oil Spill Detection

A large part of oil spills happen near busy marine fairways. Presently, oil spill detection and monitoring are mostly done with satellite remote sensing algorithms, or with remote sensors or visual surveillance from aerial vehicles or ships. These techniques have their drawbacks and limitations. We evaluated the feasibility of using fluorometric sensors in flow-through systems for real-time detection of oil spills. The sensors were capable of detecting diesel oil for at least 20 days in laboratory conditions, but the presence of CDOM, turbidity and algae-derived substances substantially affected the detection capabilities. Algae extract was observed to have the strongest effect on the fluorescence signal, enhancing the signal in all combinations of sensors and solutions. The sensors were then integrated to a FerryBox system and a moored SmartBuoy. The field tests support the results of the laboratory experiments, namely that the primary source of the measured variation was the presence of interference compounds. The 2 month experiments data did not reveal peaks indicative of oil spills. Both autonomous systems worked well, providing real-time data. The main uncertainty is how the sensors' calibration and specificity to oil, and the measurement depth, affects oil detection. We recommend exploring mathematical approaches and more advanced sensors to correct for natural interferences.

scholarly journals Characterization of microbial response to petroleum hydrocarbon contamination in a lacustrine ecosystem

2021 ◽  
Author(s):  
Emilio D’Ugo ◽  
Milena Bruno ◽  
Arghya Mukherjee ◽  
Dhrubajyoti Chattopadhyay ◽  
Roberto Giuseppetti ◽  
...  
Keyword(s):  
Oil Spill ◽  
Petroleum Hydrocarbon ◽  
Oil Spills ◽  
Oil Pollution ◽  
Primary Source ◽  
Hydrocarbon Contamination ◽  
Petroleum Hydrocarbon Contamination ◽  
Microbial Response ◽  
Pollution Event ◽  
Hydrocarbonoclastic Potential

AbstractMicrobiomes of freshwater basins intended for human use remain poorly studied, with very little known about the microbial response to in situ oil spills. Lake Pertusillo is an artificial freshwater reservoir in Basilicata, Italy, and serves as the primary source of drinking water for more than one and a half million people in the region. Notably, it is located in close proximity to one of the largest oil extraction plants in Europe. The lake suffered a major oil spill in 2017, where approximately 400 tons of crude oil spilled into the lake; importantly, the pollution event provided a rare opportunity to study how the lacustrine microbiome responds to petroleum hydrocarbon contamination. Water samples were collected from Lake Pertusillo 10 months prior to and 3 months after the accident. The presence of hydrocarbons was verified and the taxonomic and functional aspects of the lake microbiome were assessed. The analysis revealed specialized successional patterns of lake microbial communities that were potentially capable of degrading complex, recalcitrant hydrocarbons, including aromatic, chloroaromatic, nitroaromatic, and sulfur containing aromatic hydrocarbons. Our findings indicated that changes in the freshwater microbial community were associated with the oil pollution event, where microbial patterns identified in the lacustrine microbiome 3 months after the oil spill were representative of its hydrocarbonoclastic potential and may serve as effective proxies for lacustrine oil pollution.

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.

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.

Novel Electro-Optic Imaging Technologies for Day/Night Oil Spill Detection

2014 ◽  
Vol 2014 (1) ◽  
pp. 299609
Author(s):  
Toomas H. Allik ◽  
Roberta E. Dixon ◽  
Lenard V. Ramboyong ◽  
Mark Roberts ◽  
Thomas J. Soyka ◽  
...  
Keyword(s):  
Oil Spill ◽  
Infrared Imaging ◽  
Imaging Techniques ◽  
Spectral Characteristics ◽  
Laboratory Data ◽  
Field Tests ◽  
Light Level ◽  
Test Facility ◽  
Imaging Technologies ◽  
Oil Spill Detection

Joint program between the U.S. Departments of the Interior and Defense to bring knowledge, expertise and military, low-light level and hyperspectral imaging technologies to remote oil spill detection. Program emphasis is to determine remote infrared imaging techniques for the quantification of oil spill thickness. Spectral characteristics of various crude oils in the SWIR (1–2 microns), MWIR (3–5 microns) and LWIR (8–12 microns) were measured. Analysis of laboratory data and Deepwater Horizon hyperspectral imagery showed the utility of the SWIR region to detect crude oil and emulsions. We have evaluated two SWIR wavelengths (1200 nm and 1250 nm) for thickness assessment. An infrared, 3-color imager is discussed along with field tests at the BSEE's Ohmsett test facility.

Using dogs to detect oil spills hidden in snow and ice - A new tool to detect oil in Arctic environments

2017 ◽  
Vol 2017 (1) ◽  
pp. 2219-2236
Author(s):  
Per Johan Brandvik ◽  
Turid Buvik
Keyword(s):  
Oil Spill ◽  
Oil Spills ◽  
Single Point ◽  
Field Tests ◽  
Point Sources ◽  
Remote Areas ◽  
Search Site ◽  
Arctic Conditions ◽  
Large Aircraft ◽  
Snow And Ice

ABSTRACT The main objective of this project has been to train dogs to find oil spills hidden in snow or ice. Previous tests performed during 2007 in a laboratory environment in Trondheim showed that dogs are able to detect and identify the smell of oil, both weathered crude and bunker fuels. Outdoor tests in the Trondheim area in Norway (63°N) have also shown that dogs detect the smell of oil and can find point sources of oil at an outdoor temperature down to −5°C. This was confirmed in phase I of this project. Realistic field tests conducted in 2008 on Svalbard (78°N) confirmed that dogs can be used to detect oil spills covered with snow and ice in Arctic environments. The dogs were able to locate single point sources and determine the approximate dimensions of a larger oil spill. The dogs also verified the bearing to a larger oil spill (400 liters, covered in snow) in increasing downwind distances up to 5 km from the oil spill. This fieldwork on Svalbard has shown that the search dog teams perform well under very harsh Arctic conditions. The dogs and the handlers were able to work in temperatures below −20° C for multiple days. The dogs also managed to keep their full concentration and operative sensitivity for several days even after being transported, first by large aircraft (3 hours), then by small aircraft (0.5 hour) and finally the search site in cages strapped on snow scooter sledges. The use of snow scooters for transporting the dogs made it possible to reach remote areas, arriving with rested dogs ready for action. This study has showed that specially trained dogs are a sensitive and effective tool to search large snow and ice covered areas to detect possible oil spills.

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.

Real-Time Architecture for an Electro-Mech-Optical System for Detection of Vehicles on Highway

2004 ◽  
Author(s):  
Zhaoqing Wang ◽  
Harry H. Cheng ◽  
Stephen S. Nestinger ◽  
Benjamin D. Shaw ◽  
Joe Palen
Keyword(s):  
Travel Time ◽  
Real Time ◽  
Intelligent Transportation System ◽  
Detection System ◽  
Field Tests ◽  
Laser Source ◽  
Time Data ◽  
Time Signals ◽  
Message Exchange ◽  
User Space

A real-time architecture for a highway vehicle detection system is presented in the article. The Laser Based Detection System (LBDS), focused on helping the Intelligent Transportation System (ITS), measures a key quantitative parameter of vehicles moving across a link of highway, namely, travel time. Travel time is based upon the identifying and reidentifying vehicles at various points on the highway. This article provides a method to collect real-time signals from an active laser source in the LBDS and calculate vehicle parameters using a standard computer. A method of message exchange between a real-time kernel process, for real-time data acquisition, and a user space process, for computing and displaying, is given under the RTLinux environment. Experimental results from field tests have shown that the application of the real-time architecture to the LBDS provides speeds deterministically.

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.

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