Data assimilation of an airborne multiple-remote-sensor system and of satellite images for the North Sea and Baltic Sea

Trieschmann, Olaf; Hunsaenger, Thomas; Tufte, Lars; Barjenbruch, Ulrich

doi:10.1117/12.514023

Cited by 9 publications

(5 citation statements)

References 4 publications

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“…While spaceborne remote sensing has a higher efficiency for large area coverage, airborne surveillance is less efficient because of the limited area coverage. Comparatively, the latter is more suitable and efficient for identification of source, type, thickness, and extent of oil spills with higher spatial resolution [10,[59][60][61] unlike the former with a lower spatial and temporal resolution but useful for monitoring remote areas. In the following sections, different oil spill detection sensors are reviewed, stating the underlying principles, advantages, and limitations of each sensor.…”

Section: Remote Sensing In Oil Spill Managementmentioning

confidence: 99%

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

Yekeen

Balogun

2020

Remote Sensing

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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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Section: Remote Sensing In Oil Spill Managementmentioning

confidence: 99%

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

Yekeen

Balogun

2020

Remote Sensing

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“…The "57+04" carries as special equipment a laser fluorescence sensor and a microwave radiometer on board (for the "57+04", the reader is referred to [25], and more details can be found in, e.g. [26][27][28], and for a general review of airborne remote sensing of oil spills to [15,29,30]). …”

Section: Airborne Sensorsmentioning

confidence: 99%

The German Operational Monitoring System in the North Sea: Sensors, Methods and Example Data

Baschek¹,

Gade²,

Bernem³

et al. 2015

The Handbook of Environmental Chemistry

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Operational oil pollution surveillance has been performed in Germany for almost 30 years. Sophisticated state-of-the-art sensors are being used for frequent airborne surveillance, while satellite data are used as prewarning and additional information input on a routine basis. In parallel, basic research on the imaging of marine oil pollution by synthetic aperture radar (SAR) has been performed, and a basic understanding of the imaging of biogenic and anthropogenic marine surface films by active microwave sensors has been developed. In this paper, we provide an overview of the current operational surveillance system, and we give some historical background summarising some of the results of the research conducted during the past decades. Within this chapter, example images from pollution events are given for several sensors. The German coast's spatial and temporal vulnerability to oil pollution is quantified, and the use of dispersants in a highly vulnerable ecosystem such as the "Wadden Sea" is discussed.

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“…②The passive Microwave radiometers observe microwave radiation from centimeter wave band to millimeter wave band emitting from sea surface, and they are free from weather stipulation [10] . Floating oil can emit stronger microwave radiation than water and looks like bright object in the ocean.…”

Section: Common Data For Oil Spill Monitoringmentioning

confidence: 99%

Experiment of monitoring oil spill on the base of EOS/MODIS data

et al. 2010

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The petroleum pollution is one of the main pollutants of Chinese ocean, and developing algorithms and systems for realtime oil spill monitoring is an urgent matter of the moment. Because the satellite remote sensing is efficient, fargoing and inexpensive, this paper is focused on making use of satellite data of EOS/MODIS, and attempting to monitor oil spill of maritime space in China. The method is built based on the analysis of oil spectral characteristic, and then the remote sensing spectral characters of several petroleum types under the maritime circumstance and the difference of thermal infrared spectrum of oil are observed to provide oil information for oil film monitoring. The research will be used water quality monitoring and early warning for larger ocean disaster, and shows a good application example of satellite data of the EOS/MODIS.

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Data assimilation of an airborne multiple-remote-sensor system and of satellite images for the North Sea and Baltic Sea

Cited by 9 publications

References 4 publications

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

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

The German Operational Monitoring System in the North Sea: Sensors, Methods and Example Data

Experiment of monitoring oil spill on the base of EOS/MODIS data

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