Remote sensing techniques present the opportunity to monitor environmental risks posed by oil pollution
Distinguishing oil slicks from lookalikes continues to be a complex issue surrounding oil spill detection
Detecting and understanding the spatial and temporal variability of oil slicks is key for planning offshore petroleum exploration and crucial in identifying anthropogenic oil spills to inform clean-up operations and limit environmental damage.
Both microwave radar and optical remote sensing are commonly used to monitor marine oil pollution. Synthetic-aperture radar (SAR) is a particularly effective technique, owing to its wide coverage area and all-weather, day and night imagery acquisition capability. However, SAR suffers from an inability to distinguish between slicks, and ‘lookalikes’ – other dark spots on the radar image arising from natural processes such as algal blooms and upwelling zones.
Optical remote sensing (ORS) techniques can provide clarity on oil slick characterisation, but suffer from restrictive environmental conditions, as they can only be used in stringent clear weather and daylight hours. Given these remote sensing limitations, distinguishing oil slicks from lookalikes continues to be a complex issue surrounding oil spill detection.
Aiming to further develop the ability to characterise oil slicks, Bayramov, Kada & Buchroithner (2018) used a combination of SAR and ORS techniques, to evaluate oil slicks in the Caspian Sea. Considered the ‘world’s largest lake’, the Caspian Sea has a long-standing history of oil exploration dating back to 1870 and with both new and old oil infrastructure currently present, it provides an excellent site for oil slick studies.
Using satellite data gleaned from over 411 satellite images acquired between 1996 and 2017, they were able to characterise the spatial and temporal distribution of both anthropogenic and natural seepage slick occurrence. Additionally, they were able to predict the risk such slicks would pose to water quality and shoreline ecosystems using stochastic modelling.
The results revealed different sections of the Caspian Sea are at risk of different slick types. Interestingly, natural oil seepage was most prevalent in the southern part of the Caspian Sea. The Azerbaijani portion of the sea, however, was identified as the most at-risk from oil spills, with multiple anthropogenic spill hotspots identified, both in spatial extent and frequency of occurrence.
The three oldest oil platforms formed the centre of these oil spill hotspots, producing potential spill rates of up to 1265 m3 per day. Modelling suggested there was a greater than 50% contamination risk in the shoreline range of 464-508 km with this range also suffering the highest maximum accumulated emulsion mass in the event of a spill.
During the verification of these shoreline contamination predictions using satellite images, a staggering 32 out of 44 km of shoreline with a predicted contamination risk of greater than 50% was shown to be affected by relatively frequent oil pollution occurrences. With much of this stretch of shoreline falling within protected areas, this poses a significant risk to resident biodiversity during a contamination event.
The study highlights how the combination SAR and ORS can be used to overcome the drawbacks of both technique’s individual limitations when used in isolation. While specific to the Caspian Sea, the conclusions also illustrate how remote sensing data can be used to quantitatively assess the risk of oil slicks and spills to shoreline areas of high environmental value.
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