Hyperspectral detection of methane stressed vegetation

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.

[1]  A. Huete,et al.  Overview of the radiometric and biophysical performance of the MODIS vegetation indices , 2002 .

[2]  R. King,et al.  The analysis of hyperspectral data using Savitzky-Golay filtering-theoretical basis. 1 , 1999, IEEE 1999 International Geoscience and Remote Sensing Symposium. IGARSS'99 (Cat. No.99CH36293).

[3]  Alexandar Krumov,et al.  Assessment of plant vitality detection through fluorescence and reflectance imagery , 2008 .

[4]  Pol Coppin,et al.  Detection of biotic stress (Venturia inaequalis) in apple trees using hyperspectral data: Non-parametric statistical approaches and physiological implications , 2007 .

[5]  Craig S. T. Daughtry,et al.  A visible band index for remote sensing leaf chlorophyll content at the canopy scale , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[6]  J. Hoeks,et al.  CHANGES IN COMPOSITION OF SOIL AIR NEAR LEAKS IN NATURAL GAS MAINS , 1972 .

[7]  C. Tucker.  Remote sensing of leaf water content in the near infrared , 1980 .

[8]  Ismael Moya,et al.  A new instrument for passive remote sensing: 2. Measurement of leaf and canopy reflectance changes at 531 nm and their relationship with photosynthesis and chlorophyll fluorescence , 2004 .

[9]  Donghyeon Yu,et al.  Classification of spectral data using fused lasso logistic regression , 2015 .

[10]  J. R. Jensen,et al.  Hyperspectral Remote Sensing of Vegetation , 2008 .

[11]  Andrew K. Skidmore,et al.  Continuum removed band depth analysis for detecting the effects of natural gas, methane and ethane on maize reflectance , 2006 .

[12]  Raymond F. Kokaly,et al.  Spectral Analysis of Absorption Features for Mapping Vegetation Cover and Microbial Communities in Yellowstone National Park Using AVIRIS Data , 2007 .

[13]  Jinsong Chen,et al.  Research on effectiveness of hyperspectral data on identifying rice of different genotypes , 2009 .

[14]  C. Field,et al.  A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency , 1992 .

[15]  J. J. Colls,et al.  Use of hyperspectral derivative ratios in the red-edge region to identify plant stress responses to gas leaks , 2004 .

[16]  Michael E. Schaepman,et al.  Estimating canopy water content using hyperspectral remote sensing data , 2010, Int. J. Appl. Earth Obs. Geoinformation.

[17]  Kenji Omasa,et al.  Comparing vegetation indices for remote chlorophyll measurement of white poplar and Chinese elm leaves with different adaxial and abaxial surfaces , 2015, Journal of experimental botany.