Research Categories / Remote Sensing

Oil spill detection under ice and on seafloor

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.

Testing of Oil Spill Technologies (TOST) Program

This new project will develop a program for evaluating oil pollution mitigation technologies to provide performance data to stakeholders to facilitate decision making for oil spill preparedness and response operations. Data will be collected through systematic and unbiased testing and disseminated to stakeholders and to the public. BSEE is working with the USCG to initiate this program to conduct testing in October 2022.

Advancement of MARINE SCOUT

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 oil on water, distinguishing it from false postives such as kelp forests, and providing a reliable estimate of the thickness of the oil. The purpose of this project is to advance the current MARINE SCOUT payload for algorithm development and semi-automation for (near) real-time data processing.

LiDAR Oil Characterization and Automated Software Development

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.

The Web based General NOAA Oil Modeling Environment (WebGNOME) Anywhere

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.

An Adaptable Frequency Modulated Continuous Wave (FMCW) Radar For Unmanned Aerial Systems To Detect Oil In Sea Ice

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.

Algorithm Development for (Near) Real Time Data Processing and Mapping for Remote Sensors

Real-time data processing is critical to decision making. Although various remote sensors to detect oil slicks have been developed, the advanced, processing/analyzing data and imagery requires significant time (at least several hours to days). The purpose of this project is to develop/advance algorithm for (Near) Real-Time Data Processing and Mapping for Commercially available Off The Shelf (COTS) Remote Sensors to detect oil and measure slick thickness. 


This project will study and test 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, AUB proposes a planar sensor design where the electrodes are mounted on a single plane and placed at a relatively close distance above the ice surface. 

Canine Oil Detection – Using Odor Signatures to Improve Training Detection Proficiency on Land and Water

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.

Three-dimensional mapping of dissolved hydrocarbons and oil droplets using a REMUS-600 (Remote Environmental Monitoring Unit) AUV (Autonomous Underwater Vehicle)

The goals of this project were to (1) integrate a suite of sensors on a REMUS AUV to quantify, characterize and determine droplet size of spilled oil, (2) demonstrate the utility of this technology for oil detection in the field, and (3) develop a schema for real-time data transfer into existing spill response data management and visualization tools.