Classification of Oil Spill Thicknesses Using Multispectral UAS And Satellite Remote Sensing for Oil Spill Response

Abstract

Unmanned Aerial Systems (UAS) are an operational tool for monitoring and assessment of oil spills. At the same time, satellite imagery has been used almost entirely to detect oil presence/absence, yet its ability to discriminate oil emulsions within a detected oil slick has not been fully exploited. Additionally, one of the challenges in the past has been the ability to deliver strategic information derived from satellite remote sensing in a timely fashion to responders in the field. This study presents UAS and satellite methods for the rapid classification of oil types and thicknesses, from which information about thick oil and oil emulsions (i.e., actionable oil) can be delivered in an operational timeframe to responders in the field. Experiments carried out at the OHMSETT test facility in New Jersey demonstrate that under specific viewing conditions satellites can record a signal variance between oil thicknesses and emulsions and non-emulsified oil. Furthermore, multispectral satellite data acquired by RADARSAT-2 and WorldView-2 were combined with data from a UAS field campaign to generate an oil/emulsion thickness classification based on a multispectral classification algorithm. Herein we present the classification methods to generate oil thickness products from UAS, validated by sea-truth observations, and quasi-synoptic multispectral satellite images acquired by WorldView-2. We tested the ability to deliver these products with minimum latency to responding vessels. During field operations in the Gulf of Mexico, we utilized the UAS multispectral system to identify areas of shoreline impacted by the oil spill. This proof-of-concept test using multispectral UAS data to detect emulsions and deliver a derived information product to a vessel in near-real-time sheds light on how UAS assets could be used in the near future for oil spill tactical response operations.