Hyperspectral and Thermal Remote Sensing of Plant Stress Reponses to Oil Pollution.

Emengini, Ebele Josephine (2010) Hyperspectral and Thermal Remote Sensing of Plant Stress Reponses to Oil Pollution. PhD thesis, Lancaster University.

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Abstract

This study investigates the potential use of hyperspectral and thermal remote sensing for the early pre-visual detection and quantification of plant stress caused by oil pollution. Further, it examines the potential for these techniques to discriminate between oil pollution and two typically encountered plant stresses of waterlogging and water deficit. Results show that oil pollution, waterlogging and water deficit significantly decreased the physiological functions of plants and can result in pre-visual changes in spectral and thermal responses. Various spectral indices such as (R755-R716)/(R755+R716) and R800/R6O6 were efficient for the early detection of oil-induced stress in maize (up to 10 days earlier) and bean (up to 4 days earlier), respectively. These indices and other simple ratios of reflectance such as R673/R545 were also sensitive in the early detection (up to 6 days earlier) of stress symptoms caused by waterlogging in bean. The canopy absolute temperature and thermal index (IG) were good indicators of oil related stress in bean, but were insensitive to waterlogging. Absolute leaf temperature had minimal potential for detecting oil pollution in maize. While the spectral indices lacked ability for the early detection of stress caused by water deficit at the leaf scale in both maize and bean, absolute temperature was effective in this regard irrespective of scale of measurement. Results show that by combining spectral and thermal information, oil pollution can be discriminated from waterlogging or water deficit treatment. This study concludes that hyperspectral and thermal remote sensing have the potential to detect and quantify plant stress caused by oil pollution and it is possible to discriminate between this and other common stresses. However, further work is needed to refine and operationalise the approach, and the problems and challenges associated with this are presented and discussed.