Remote Sensing

Real-time data processing is critical to decision making. Although various remote sensors to detect oil slicks have been developed, processing/analyzing data and imagery requires significant time (at least several hours to days). The purpose of this project was 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.

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.

This project developed oil thickness sensors to quantify the thickness of oil floating on water. Two unique sensors were developed to measure oil thickness greater than 3mm in real time. The first is a hand held unit that can be deployed from a vessel or used in a test environment. Measurements are read by the user directly from the sensor. The second is designed for mounting on a skimmer, buoy, or oil containment boom, and is designed to measure in wave conditions. This sensor transmits oil thickness measurements wirelessly to a user up to a distance of 200-250 meters.

The focus of this project will be on the design and implementation of two components, the UAS system, and the algorithms for the image processing used on the system. The project will be carried out in two phases:  Phase 1: Development/Implementation of the UAS platform/sensors and its algorithms for oil classification and image processing based on Ohmsett testing.  Phase 1 will involve the following sub-tasks: 1) UAS multisensory array implementation, 2) Controlled experiment (Ohmsett tank testing), and 3) Development of the oil classification and image processing algorithm.

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.
 

The purpose of this study was to assess and evaluate the capabilities and limitations of two above-water laser systems owned and operated by NRL, to detect and characterize oil layers of varying thickness on the surface of the water, in conjunction with an acoustic sensor for in-water detection (oil in the water column).

The accurate monitoring of subsea oil release droplet sizes and the effects of applying subsea dispersants is a technology gap with consequences for decision-making, impact assessment, and scientific understanding of blowout behavior. Measurements and knowledge of the actual droplet sizes that exist under differing blowout or subsea release scenarios are fundamental to response decision-making and understanding potential ecosystem impacts.
 

This project developed two sensors for measuring the thickness of oil on water. The first, a capacitive sensor, measures 3 to 100 millimeter (mm) thick oil layers while mounted to a skimmer, in the apex of a boom, or on a free-floating buoy and provides near real-time wireless communication of thickness information. This sensor can also be used during experimentation or testing to verify oil thickness. The second sensor measures very thin sheens of oil, between 100 micrometer to 3mm, and is designed as a free-floating sensor.

The objective of this project is to conduct an oil spill response viability analysis for the U.S. Outer Continental Shelf (OCS) Gulf of Mexico (GOM).  This analysis will quantify the frequency and duration that a specific oil spill response strategy may not be feasible or ‘unduly’ impacted such that response effectiveness is judged to be degraded due to metocean conditions.  Conditions to be considered in the analysis include wind, sea state, salinity,  and visibility using available hindcast environmental data.

BSEE has teamed up with NOAA to provide control and validation for surface oiling characterization efforts. The ultimate goal was to validate and quantify the capabilities of various remote sensing systems and sensors, provide BSEE and NOAA the needed methodology and operational tools to assess future oil spills, and the ability to monitor and measure more accurately the thickness of surface oil slicks in the marine environment.