GOM WM OST 20161116

These data were collected as part of the National Oceanic and Atmospheric’s (NOAA) DWH Lessons Learned Studies: Detection of Oil Thickness and Emulsion Mixtures using Remote Sensing Platforms study on methods to estimate oil slick coverage and thickness. The Team developed methods for synoptic collection of satellite imagery, airborne imagery, surface oil characterization, oil and water chemistry, and subsurface oil slick data at both the Oil and Hazardous Materials Simulated Environmental Test Tank (Ohmsett) and the Mississippi Canyon lease block #20 (MC20), which has experienced an ongoing chronic oil discharge since 2004. Data shown here in NOAA’s Environmental Response Management Applications (ERMA) are part of the MC20 field research undertaken in 2016, 2017, and 2018. This research was primarily funded by the U.S. Department of the Interior, the Bureau of Safety and Environmental Enforcement (BSEE), and the Oil Spill Preparedness Division through Interagency Agreement E16PG00023 with the U.S. Department of Commerce, NOAA.

The primary objective of this research was to compare the ability of multiple remote sensing platforms to detect and quantify surface oil, and verify that anomalies identified in remote sensing images corresponded with oil slick features that could be observed and quantified in situ. This research was organized into 3 phases:

Phase 1: Characterized the detection of known oil thicknesses and oil-emulsions in a controlled environment, by performing multiple tests and calibrations for thermal, optical, and microwave sensors at the National Oil Spill Response Research & Renewable Energy Test Facility (Ohmsett) which is located at the Naval Weapons Station Earle Waterfront in Leonardo, New Jersey. Controlled experiments took place during July 2016.

Phase 2: Measured the open water oil thicknesses and oil-emulsions at the damaged Taylor Energy well field surface oiling site (MC20) by performing multiple tests and calibrations for thermal, optical and microwave sensors. Data were collected in November 2016, April 2017, and August 2017.

Phase 3: Developed operational methods and procedures for processing and interpreting each of the sensors products used during the experiments for future emergency operations.

The objective of synoptic sampling at MC20 was to provide data and observations on the water that verified (ground-truth) remote sensing data. The Team attempted to collect as much multi-platform and sensor data as was practicable while scheduling field sampling events to coincide with satellite collections. The Team also collected imagery from Unmanned Aerial Systems (UAS) and as many as four separate fixed-wing aircraft timed to these collections. These data were collected by multiple researchers from NOAA, Ocean Imaging Corp., Water Mapping LLC, University of North Texas (UNT), EPA, WHOI, Fototerra Aerial Survey LLC, and others. A total of 5 different data types are shown here in ERMA: in situ sampling, ship/flight trackline and photos, imagery, oil characterization, and oil on water samples. Summaries of the data collection methods are included below.

The primary purpose of the in situ sampling at the Ohmsett facility and at the MC20 site was to develop methods for characterizing slick thickness and calibrating remote sensing data. Since oil is inherently heterogeneous on any scale, many thickness samples are required to provide the range of oil thicknesses over a given area. NOAA tested three different methods for measuring the thickness of oil slicks or sheens: the dip plate, the sorbent pad, and the Water Mapping sampler. The three methods tested as part of this research have different advantages and disadvantages:

The dip plate method of measuring thickness is fast and inexpensive; however, the method has a limited range of thicknesses for which it is accurate.

The sorbent pad method of measuring thickness provides the most accurate estimate of slick thickness for thin sheens. It is less accurate in thicker oil, and the need for laboratory analysis of the pads makes it relatively slow and expensive to obtain data.

The Water Mapping method is the most accurate of the three methods. However, sampling using this method is time-consuming, as the sampler can collect only one sample covering a small area, and each sample requires the addition of a solvent, a photograph in a controlled setting, and a manual pixel-based measurement to measure thickness.

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When measuring slicks side-by-side in a controlled laboratory environment, we found that all three methods accurately and precisely measured slick thickness within a factor of two.

However, when we used these methods at the Ohmsett facility or in the field, we frequently had thickness measurements that varied by 1–2 orders of magnitude despite being collected at the same time and place. The variability seen in these measurements likely represents the patchiness of the slick being sampled (see, for example, Figure 3.24). The wide range of in situ measurements at coincident locations complicates defining a single slick thickness for a specific location.

TBD

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