Spectral Indices Survey for Oil Spill Detection in Coastal Areas

Abstract

When oil spills occur at sea, effective detection and monitoring are required to establish a successful response plan, and remote sensing emerges as an appropriate technology to support this process. Furthermore, Earth observation is progressively relying on low-cost multispectral sensors developed for monitoring particular features across various scenarios. However, despite these advancements, the ongoing challenge lies in reducing computational costs, resource requirements and energy consumption. This work aims to select the spectral index that best detects coastal spills among those documented. The long-term goal is to develop a low-cost multispectral sensor with suitable bands. For this purpose, this study uses data from different sensors that acquired data of the Deepwater Horizon accident in the Gulf of Mexico. The confusion matrices, accuracy, and F1-score generated from the kNN pixel classification based on the indices values are measures of its performance. For this study, the recently introduced Normalized Difference Oil Index (NDOI) proves to be the best option for identifying coastal spills since it minimises the false positives related to suspended sand and is quick to calculate. In addition, it has demonstrated that it helps distinguish different spill thicknesses and estimates the oil volume. Therefore, future research will focus on developing and validating a low-cost multispectral system that uses the NDOI bands to detect spills.