Past Awards

FY 2022 | 1 – Understand | 22-10-07

Analysis of data for oil in ice

Watermapping LLC
Contract Term: 04/01/22-09/30/22
Award: $

This project seeks to advance oil spill emergency response by developing an
unexpensive fluorosensing UAS system (of less than $10K) capable of detecting the presence of
oil in water. Tests will also be conducted to examine the system’s capabilities to detect the
relative thickness of the target oil, and provide near real time oil extent and thickness maps
after the completion of a data collection survey. Furthermore, the designed portable UAS
fluorosensor system will be capable of being used synchronously with other UAS sensors
including Thermal, RGB, Multispectral, and Lidar.
This project proposal contains three main sections. In section 1 we explain the science behind
the concept of fluorescence method for detection of floating oil on water. In section 2, we
describe the technical details of the engineering for the system development, and lastly section
3, we describe how we plan testing the system on multiple scenarios in control setting low
illumination conditions and on the field.
As a summary, the sensor system will consist mostly on hardware and software integration
including: lasers, optics, a spectrophotometer, and microcomputers. The bulk of this research
effort will be focused on the testing and operationalization of the system. At our lab, in order to
build and bench test the system, we will reproduce multiple settings of floating oil layers (with
different configurations of oil thicknesses) for marine diesel, fresh crude oil, and heavy
emulsified oil. For the field testing, we will work locally on a safe location where the system will
be tested at night by using tanks on the ground, where we will be testing the multiple
configurations of floating hydrocarbons previously mentioned. Extensive testing will be
conducted firstly for the calibration of the sensor, and secondly for the comparison of the
sensor capacity against other imaging techniques for ‘low light’ detection of oil including
multispectral and thermal imagers. Additionally, this proposed work will report a detailed
statistical analysis among the performance of all the systems during low visibility conditions.
We will also report the progression of detection of the system as a timelapse in which we
observe the pulse of fluorescence reflected by oil capturing the effect of change on illumination