Remote Sensing

This Multi Partner Research Initiative (MPRI) project was a collaborative effort between BSEE, National Oceanic Atmospheric Administration (NOAA), Fisheries and Oceans Canada (DFO), the US Coast Guard (USCG), the Environmental Protection Agency (EPA), University of New Hampshire Coastal Response Research Center (CRRC), Oil Spill Recovery Institute (OSRI), and Johns Hopkins to advance the response community’s ability to characterize and measure oil slick thickness through collaboration, knowledge sharing, and independent systematic technology assessment and testing.

The project:

Under OSRR Project 1091, the NRL performed preliminary experiments to assess pulsed laser light technology (Light Detection And Ranging - LiDAR) ability to detect oil and characterize oil thickness on water. Initial testing conducted at Ohmsett demonstrated the successful application of LiDAR remote sensing to detect and measure the presence of oil at the surface and underwater.

This project will continue the development of the LiDAR system's ability to detect and characterize oil on the surface and varying subsurface layers thickness values and depth in the water.

Although remote sensing technologies have been advanced for airborne and spaceborne sensors, it is still challenging to detect oil under/encapsulated in ice as well as on seafloor.

The objectives of this project are to:

Investigate and advance the current underwater technology to detect and measure thickness of oil under ice, encapsulated in ice and/or on the seafloor
Conduct testing at Ohmsett or CRREL to characterize the sensitivity of remote sensors for detecting and characterizing oil under ice, encapsulated in ice, and/or oil on the tank floor.

Under Interagency Agreement (IAA) E13PG00031, the Bureau of Safety and Environmental Enforcement (BSEE)/U.S. Army Night Vision team completed a program that demonstrated a compact, lightweight, multi-spectral airborne sensor payload capable of detecting the presence of oil on water, distinguishing oil from false alarms such as kelp forests, and providing a reliable estimate of the thickness of the oil present on the water.

This project tested the Electrical Capacitance Tomography (ECT) sensor to detect oil in/under ice. For oil detection and thickness estimation under/in ice, where the access to the imaged region is limited to above its surface, the project team used a planar sensor design where the electrodes are mounted on a single plane and placed at a relatively close distance above the ice surface. 

The current NOAA's WebGNOME platform displays the modeling bounds with available operational forecast models for selected areas. These areas are typically in shoreline areas. This project will expand the availability of forecast models to cover offshore areas where BSEE's regulated facilities reside. This added feature will enable the ability to run WebGNOME more easily, using available operational forecast models.

The project team will use radar technology instead of optical or infrared methods in order to enable 24-hour, weather independent operation that can be deployed in inclement or difficult to access environments, and reduce dependence upon on-site personnel. The team will evaluate the capability of low noise L-band (1.26 GHz) synthetic aperture radar (SAR) imagery acquired by the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) sensor.
 

A workshop is being developed to discuss impacts of oil spills on shorelines. The workshop will bring together Subject Matter Experts (SME) from the spill response community, academia, and industry for the exchange of ideas and the exploration of the current state of the science of oil spill research. Traditional oil and gas exploration and production (E&P) as well as renewable energy (RE) will be explored. The results of this workshop will help guide BSEE Oil Spill Response Research (OSRR) to develop relevant Research and Technologies (R&T) in fulfillment of BSEE's mission.

The objectives of this research are first to determine the odor profile associated with spilled and obscured petroleum products used by the canine for detection and then use this knowledge to probe current canine detection limitations. The Naval Research Laboratory will develop and optimize methods of analysis for weathered crude oil using solid phase microextraction (SPME) with gas chromatography and mass spectrometry (GC-MS), and liquid injection with GC-MS for odor profile assessment. Chiron K9 will perform all canine training and testing.

As Arctic ice has receded, exploration and development of oil reserves have increased, thereby requiring an effective strategy to mitigate oil spills. PNNL proposes demonstrating oil detection in and under sea ice via FMCW radar by leveraging recent advancements in commercial subcomponents and systems. Utilizing Commercial Off the Shelf (COTS) hardware will address hardware reliability issues and focus work on implementation challenges.