Oil Spill Source Identification and Impact Assessment on Marine Environment Using Remote Sensing and Spectral Analysis: A Case Study of the Persian Gul

Abstract

Identifying the source of oil spills and monitoring their impact represents critical environmental challenges in marine ecosystems, particularly within the Persian Gulf. Although traditional field- and observation-based methods are valuable, they remain limited in distinguishing oil slicks of varying thicknesses and in accurately assessing their environmental impacts. Therefore, this study employed Sentinel-2 multispectral satellite imagery acquired on July 6, 2020, corresponding to an oil spill event in the Forouzan oil field, to perform source identification and thickness differentiation of oil slicks. Atmospheric corrections were carried out using the FLAASH and QUAC modules, and the Otsu thresholding method was integrated with the Maximum Likelihood Classification algorithm, yielding an overall accuracy of 82.14% and a Kappa coefficient of 0.715. Spectral separability analysis (IS) revealed that the Fluorescence Index (FI) exhibited the highest discrimination capability, with IS values of 1.542, 0.967, 0.423, and 0.4236 for distinguishing emulsions from seawater, sheens, code 4, and code 5 classes, respectively. At the same time, the Hydrocarbon Index (HI) showed the lowest separability. The CHL and CDOM indices proved more effective in detecting thinner slicks. Furthermore, environmental indicators (OC3, FAI, NDVI, TWI) demonstrated that oil slicks caused decreases in chlorophyll concentration, floating algae abundance, and water color, along with an increase in turbidity. Overall, the findings confirm the high accuracy and effectiveness of combining spectral indices with remote sensing–based spectroscopic and classification techniques for ecological monitoring and assessment of marine oil pollution.