Long-wave infrared imaging of vegetation for detecting leaking CO2 gas

Jennifer E. Johnson; Joseph A. Shaw; Rick L. Lawrence; Paul W. Nugent; Laura M. Dobeck; Lee H. Spangler

doi:10.1117/1.JRS.6.063612

10 December 2012 Long-wave infrared imaging of vegetation for detecting leaking CO2 gas

Jennifer E. Johnson, Joseph A. Shaw, Rick L. Lawrence, Paul W. Nugent, Laura M. Dobeck, Lee H. Spangler

Author Affiliations +

Funded by: Department of Energy

Journal of Applied Remote Sensing, Vol. 6, Issue 1, 063612 (December 2012). https://doi.org/10.1117/1.JRS.6.063612

Abstract

The commercial development of uncooled-microbolometer, long-wave infrared (LWIR) imagers, combined with advanced radiometric calibration methods developed at Montana State University, has led to new uses of thermal imagery in remote sensing applications. One specific novel use of these calibrated imagers is imaging of vegetation for CO₂ gas leak detection. During a four-week period in the summer of 2011, a CO₂ leak was simulated in a test field run by the Zero Emissions Research and Technology Center in Bozeman, Montana. An LWIR imager was deployed on a scaffold before and during the CO ₂ release, viewing a vegetation test area that included regions of high and low CO₂ flux. Increased root-level CO₂ concentration caused plant stress that led to reduced thermal regulation of the vegetation, which was consistent with increased diurnal variation of IR emission observed in this study. In a linear regression, the IR data were found to have a strong relationship to the CO₂ emission and to be consistent with the location of leaking CO₂ gas. Reducing the continuous data set to one image per day weakened the regression fit, but maintained sufficient significance to indicate that this method could be implemented with once-daily airborne images.

Citation Download Citation

Jennifer E. Johnson, Joseph A. Shaw, Rick L. Lawrence, Paul W. Nugent, Laura M. Dobeck, and Lee H. Spangler "Long-wave infrared imaging of vegetation for detecting leaking CO2 gas," Journal of Applied Remote Sensing 6(1), 063612 (10 December 2012). https://doi.org/10.1117/1.JRS.6.063612

Published: 10 December 2012

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE