Estimating discharge rates of oily wastes and deterrence based on aerial surveillance data collected in western Canadian marine waters

Oil Spill Mapping from Kompsat-2 High-Resolution Image Using Directional Median Filtering and Artificial Neural Network

Oil spill accidents in marine environments have a massive impact on ecosystems. Various methods have been developed to detect oil spills using high-resolution optical imagery. However, ocean waves caused by heavy winds occurring in the accident area cause sun glint in the image, and this severely impedes the ability to detect the oil spill area. The objective of this study was to detect oil spill areas from high-resolution optic images using the artificial neural network (ANN) through effective suppression of severe sun glint effects. To enable this, a directional median filter (DMF) was adapted, and its use was compared with that of a traditional low-pass filter. A performance test was conducted using a KOMPSAT-2 image acquired during oil spill accidents that occurred in the Gulf of Mexico in 2010. The proposed method involved two main steps: (i) The sun glint effects caused by the ocean waves were corrected using the DMF; and (ii) the ANN approach was used to detect the oil spill area. The results show the following: (i) The designed DMF, which considers the size and angle of ocean waves, was proficient in correcting the sun glint effect in a high-resolution optical image; and (ii) oil spill areas were efficiently detected using the ANN approach with the proposed filtering method. The oil spill area was classified with accuracies of approximately 98.12% and 89.56% using the receiver operating characteristic (ROC) curve and probability of detection (POD) measurements, respectively. These results show that the accuracy of the proposed method is improved by about 9% compared to the traditional detecting algorithm.

An online platform for rapid oil outflow assessment from grounded tankers for pollution response

The risk of oil spills is an ongoing societal concern. Whereas several decision support systems exist for predicting the fate and drift of spilled oil, there is a lack of accurate models for assessing the amount of oil spilled and its temporal evolution. In order to close this gap, this paper presents an online platform for the fast assessment of tanker grounding accidents in terms of structural damage and time-dependent amount of spilled cargo oil. The simulation platform consists of the definition of accidental scenarios; the assessment of the grounding damage and the prediction of the time-dependent oil spill size. The performance of this integrated online simulation environment is exemplified through illustrative case studies representing two plausible accidental grounding scenarios in the Gulf of Finland: one resulting in oil spill of about 50 t, while in the other the inner hull remained intact and no spill occurred.

A Review of Oil Spill Remote Sensing

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.

A preliminary spatial assessment of risk: Marine birds and chronic oil pollution on Canada's Pacific coast

Chronic oil pollution poses substantial risks to marine birds and other marine wildlife worldwide. On Canada's Pacific coast, the negative ecological consequences to marine birds and marine ecosystems in general remain poorly understood. Using information relating to oil spill probability of occurrence, areas of overall importance to marine birds, and the at-sea distribution and density of 12 marine bird species and seven bird groups, including multiple Species at Risk, we undertook a spatial assessment of risk. Our results identify two main areas important to marine birds potentially at higher risk of exposure to oil. For individual bird species or species groups, those predicted to have elevated bird densities near the mainland and the northeast coast of Vancouver Island were identified as being at higher potential risk of exposure. Our results, however, should be considered preliminary. As with other anthropogenic stressors, in order to better understand and subsequently mitigate the consequences of chronic oil pollution on marine birds, improved information relating to marine birds and the occurrence of oil spills on Canada's Pacific coast is needed.

Modeling the distribution of illicit oily discharges detected by aerial surveillance in western Canadian marine waters

Oily discharges from vessel operations have been documented in Canada's Pacific region by the National Aerial Surveillance Program (NASP) since the early 1990s. We explored a number of regression methods to explain the distribution and counts per grid cell of oily discharges detected from 1998 to 2007 using independent predictor variables, while trying to address the large number of zeros present in the data. Best-fit models indicate that discharges are generally concentrated close to shore typically in association with small harbours, and with major commercial and tourist centers. Oily discharges were also concentrated in Barkley Sound and at the entrance of Juan de Fuca Strait. The identification of important factors associated with discharge patterns, and predicting discharge rates in areas with surveillance effort can be used to inform future surveillance. Model output can also be used as inputs for risk models for existing conditions and as baseline for future scenarios.

Review of oil spill remote sensing

Remote-sensing for oil spills is reviewed. The use of visible techniques is ubiquitous, however it gives only the same results as visual monitoring. Oil has no particular spectral features that would allow for identification among the many possible background interferences. Cameras are only useful to provide documentation. In daytime oil absorbs light and remits this as thermal energy at temperatures 3-8K above ambient, this is detectable by infrared (IR) cameras. Laser fluorosensors are useful instruments because of their unique capability to identify oil on backgrounds that include water, soil, weeds, ice and snow. They are the only sensor that can positively discriminate oil on most backgrounds. Radar detects oil on water by the fact that oil will dampen water-surface capillary waves under low to moderate wave/wind conditions. Radar offers the only potential for large area searches, day/night and foul weather remote sensing.