21 May 2018 Hyperspectral detection of methane stressed vegetation
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Abstract
This study examines the hyperspectral reflectance characteristics of vegetation stressed by the influence of low-level sub-terrainean methane leakage from buried pipelines. The purpose is to ascertain whether high-spatial resolution spectral imagery can be used to geolocate small methane leaks in imagery collected from small unmanned aerial systems (sUAS). This could lead to rapid detection of methane leaks by finding spectrally unique regions of stressed vegetation which might benefit a variety of industries including utility inspectors, grounds maintenance crews, and construction personnel. This document describes an experiment to manually stress vegetation by introducing methane at a low ow rate beneath a layer of turf, allowing it to percolate to the surface and affect the vitality of the overlying turf. For comparison, a turf plot was stressed by root rot caused by overwatering, as well as a sample of turf used as a control area (healthy grass). The three areas of vegetation were observed daily over the course of a one-month period with a ground spectrometer to determine the onset and time line of damage to the vegetation. High-spatial resolution spectral imagery was also collected each day to observe wavelength characteristics of the damage. First derivative analysis was used alongside physiology-based indices and logistic regression to detect differences between healthy and stressed vegetation. The hyperspectral data showed that as vegetation is stressed the red-edge slope decreases along with values through the near infrared (NIR) while the short wave infrared (SWIR) region increases. The normalized difference index (NDI) calculation of stressed vegetation in relation to healthy vegetation is maximum using a ratio of reflectance values at 750 and 1910 nm. Conclusions will be presented as to whether sUAS may be used to determine if vegetation stressed by methane can be easily detected and which spectral bands are most effective for spotting this particular stressor.
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Margot Accettura, Margot Accettura, Tim Bauch, Tim Bauch, Nina Raqueño, Nina Raqueño, Joe Mallia, Joe Mallia, Carl Salvaggio, Carl Salvaggio, } "Hyperspectral detection of methane stressed vegetation", Proc. SPIE 10664, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III, 106640I (21 May 2018); doi: 10.1117/12.2304045; https://doi.org/10.1117/12.2304045
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