Abstract

The development of small unmanned aircraft systems (sUAS) has led to a plethora of industry applications. One such application for a sUAS is detecting subterranean methane leakage. The rapid detection of methane will streamline work in industries such as construction and utilities. However, prior to flying a sUAS, the optimal way to detect methane must be determined so that unknown levels of subterranean methane leakage can be detected accurately and efficiently. In this thesis, two methods were used in conjunction to optimize a sUAS method for methane detection. The primary objective was to use hyperspectral data to locate the optimal wavelengths for methane detection for use on a sUAS. This was accomplished in two parts. The first part of the study was a simulated pipeline experiment where a copper pipe and mass flow controller were used to mimic a natural pipeline leak close to the surface. The methane-stressed and healthy vegetation were measured daily using a handheld spectrometer alongside two other forms of stressed vegetation. The analysis of the data showed potentially important variation at a two band combination of wavelengths. The second part of the study used the measured hyperspectral data as targets for a combination of atmospheric models developed using the MODerate resolution atmospheric TRANsmission (MODTRAN) algorithm at a variety of currently valid sUAS altitudes of operation. This study evaluated whether altitude will affect the ability to detect methane, along with determining which wavelength combination is best for use on a sUAS. The final assessment of an optimal application was made in regards to accuracy of methane detection within the MODTRAN data, as well as the cost analysis for industries who want to implement sUAS methane detection.

Publication Date

8-3-2018

Document Type

Thesis

Student Type

Graduate

Degree Name

Imaging Science (MS)

Department, Program, or Center

Chester F. Carlson Center for Imaging Science (COS)

Advisor

Carl Salvaggio

Advisor/Committee Member

Jan van Aardt

Advisor/Committee Member

Nina Raqueno

Campus

RIT – Main Campus

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