10 October 2008 Geospatial modelling of metocean and environmental ancillary data for the oil spill probability assessment in SAR images
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The confidence level of oil spill detections in satellite Synthetic Aperture Radar (SAR) imagery requires the analysis of many different factors. Unfortunately, oil slicks are not the only phenomena which can appear as a dark feature in a SAR image. These include a number of parameters like wind speed, currents, internal waves, upwelling sea areas, algae bloom, mixing water areas, et cetera. These phenomena are called look-alikes. The largest challenge in detecting oil spills in SAR images remains in the accurate discrimination between oil spills and look-alikes. This study introduces the vantages of using geospatial analysis of various metocean data (e.g. wind speed and direction, sea surface temperature, wave direction, ocean colour data) and environmental ancillary data (e.g. vessel traffic, port locations) as a supplementary information source for the oil spill probability assessment in SAR imagery. The analysed data exists in different formats with different value scales. In addition, the parameters of the metocean data analysis are not equally important for a reliability of oil spill detection. The weight of metocean parameters depends on the impact of natural phenomena on SAR systems (e.g. wind and currents have pro rata more influence on the probability than sea surface temperature and chlorophyll-a) and the area of interest (e.g. chlorophyll-a is a more important value for the Baltic Sea than for the Mediterranean Sea). The derived oil spill probability categorisation based on the weighted analysis of metocean environmental ancillary data could be a useful tool for authorities for an efficient planning of cost-intensive verification flights.
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Oliver Müllenhoff, Oliver Müllenhoff, Barbara Bulgarelli, Barbara Bulgarelli, Guido Ferraro, Guido Ferraro, Marko Perkovic, Marko Perkovic, Kostas Topouzelis, Kostas Topouzelis, Valerio Sammarini, Valerio Sammarini, } "Geospatial modelling of metocean and environmental ancillary data for the oil spill probability assessment in SAR images", Proc. SPIE 7110, Remote Sensing for Environmental Monitoring, GIS Applications, and Geology VIII, 71100R (10 October 2008); doi: 10.1117/12.799717; https://doi.org/10.1117/12.799717

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