Incorporating Automatic Satellite Detections of Oil Spills with Numerical Fate and Trajectory Modeling

Abstract

In recent years, access to freely available and commercial satellite imagery, such as Sentinel-1, RADARSAT-2, COSMO-SkyMed, and TerrsSAR-X, increased to the level where most global waters are observed at least once per day by one of these satellite platforms. The availability of this data combined with technological advancements in machine-learning and smart image segmentation allows for the potential to automatically detect oil spills and reduce the likelihood of false alarms. This improved satellite monitoring could result in early discovery of releases and the ability to launch a quicker response to mitigate potential damages. Numerical modeling will be used in combination with the detection results to determine the fate and trajectory of the oil as well as to hindcast where the oil was released. Implementing models into the process facilitates an effective response and incident investigation by determining where the oil is spreading and discovering where the oil originated. In 2019, Petroleum Research Newfoundland and Labrador (PRNL) launched a project led by C-CORE and RPS titled SpillSight to conduct a study into this technology for automatically detecting spills by satellite and modelling the outputs.