A Review of Oil Spill Remote Sensing

By M. Fingas, C. Brown. Italian National Conference on Sensors, 2017

582 citations recorded by the source corpus.

The technical aspects of oil spill remote sensing are examined and the practical uses and drawbacks of each technology are given with a focus on unfolding technology. The use of visible techniques is ubiquitous, but limited to certain observational conditions and simple applications. Infrared cameras offer some potential as oil spill sensors but have several limitations. Both techniques, although limited in capability, are widely used because of their increasing economy. The laser fluorosensor uniquely detects oil on substrates that include shoreline, water, soil, plants, ice, and snow. New commercial units have come out in the last few years. Radar detects calm areas on water and thus oil on water, because oil will reduce capillary waves on a water surface given moderate winds. Radar provides a unique option for wide area surveillance, all day or night and rainy/cloudy weather. Satellite-carried radars with their frequent overpass and high spatial resolution make these day–night and all-weather sensors essential for delineating both large spills and monitoring ship and platform oil discharges. Most strategic oil spill mapping is now being carried out using radar. Slick thickness measurements have been sought for many years. The operative technique at this time is the passive microwave. New techniques for calibration and verification have made these instruments more reliable.

Subfields: Global and Planetary Change, Health, Toxicology and Mutagenesis, Pollution

View Paper: https://www.semanticscholar.org/paper/0be9be93e1d8fdd84bfd311cd98ceae151184cf1

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Towards better exploiting convolutional neural networks for remote sensing scene classification (2016, background): “Methods based on deep learning have obtained state-of-the-art results in many different remote sensing applications, such as image classification [15], oil spill [17], poverty mapping [18], and urban planning [19].”
Big Data Meet Green Challenges: Big Data Toward Green Applications (2016, background): “With regard to this, Fingasa and Brownb [7] collected a variety of airborne big data in different seas where oil spills occurred previously.”