Assessment of the use of SAR and optical imagery in synergy for the detection and classification of oil spills in the North Arabian/Persian Gulf

Abstract

Effective detection of oil spill incidents is of necessity for managing and minimising the impact they have on the ecosystem, public health and economy. The rise in human population and their demands are increasing pressure making the oceans more susceptible to environmental degradation. Recent advancements of remote sensing technologies provide useful means for oil spill monitoring. Therefore, this study aims to assess the use of Sentinel-1 and Sentinel-2 satellite imageries for oil slick detection. The study examines an incident that took place in the Northern Arabian/Persian Gulf on 17 July 2017.

This study suggests that SAR imagery have better capabilities for detecting oil slicks. However, it can be prone to detect false positives such as wind shadow and algal bloom. In this study, an adaptive thresholding method was implemented on SAR imagery to delineate oil and then classified it based on thickness depending on surface roughness properties, using unsupervised machine learning classification. For verification, Optical imagery was also classified which successfully delineated oil based on its thermal features.

Spectral data from a reference library was compared with Senitnel-2 reflectance data indicate that the sensor can detect oil slick in seawater with its thermal bands. Data related to wind speed, wind direction, and ocean objects were derived from SAR imagery and were found to further validate the presence and behaviour of the slick. To enhance the accuracy of the results, further investments should continue in field sampling and aerial imagery, and more research should continue around integrating SAR and Optical datasets for oil spill detection.