Use this URL to cite or link to this record in EThOS:
Title: Hyperspectral remote sensing of canopy scale vegetation stress associated with buried gas pipelines
Author: White, Davina Cherie
ISNI:       0000 0001 3566 8893
Awarding Body: Newcastle University
Current Institution: University of Newcastle upon Tyne
Date of Award: 2007
Availability of Full Text:
Access from EThOS:
This applied study investigates the capability of field and airborne hyperspectral remote sensing to detect the spectral and spatial characteristics of sub-surface soil disturbance from associated overlying subtle canopy scale vegetation stress features. A 9 km stretch of buried gas pipeline, in Aberdeenshire, was used as a real world case study. Hyperspectral techniques, in particular derivative analysis, have a number of advantages over existing broadband approaches under operational conditions, reducing changes in illumination or background reflectance and aiding in suppressing the continuum caused by other leaf biochemicals. Various peaks in red-edge derivative spectra are also well correlated with plant pigment concentrations thus being able to detect more subtle stress features than conventional broadband reflectance approaches. The capability of these hyperspectral techniques to detect a generic stress response to gas induced soil oxygen depletion, which could also result from soil compaction and water logging due to sub-surface soil disturbance, has shown great potential under controlled conditions. However, their transferability to heterogeneous, canopy scale, field conditions for operational applications has not been published yet. This thesis aims to develop a rigorous method for spatially intensive field spectroradiometry acquisition to identify the full spatial and spectral (VIS-NIR) characteristics of subtle, canopy scale, surface vegetation stress features that maybe indicative of sub-surface soil disturbance to inform operational sensor specifications. In order to achieve this aim field spectroscopy data of barley, wheat, oilseed rape and grassland were acquired at selected transects perpendicular to a buried gas pipeline. The application of derivative analysis, vegetation band ratios and in particular the Smith et al (2004) 725:702 nm ratio, Lagrangian red-edge and continuum removal are evaluated to identify the optimal hyperspectral analytical technique for detecting vegetation stress associated sub-surface soil disturbance under operational conditions. Moreover, the ability of operational airborne hyperspectral sensors to detect the same stress features in the field data is investigated through field spectroradiometry data simulating sensor spectral Gaussian point spread functions and acquired CASI-2 imagery of the study area. First derivative analysis coupled with the 723:700 and 725:702 nm ratios was the most effective hyperspectral approach for detecting vegetation stress associated with pipeline soil disturbance. The 725:702 nm ratio of Smith et at. (2004) and a 723:700 nm ratio performed consistently well detecting stress for all sites over two consecutive field seasons under different cropping regimes, barley being particularly stress sensitive. The ratios exhibited a parabolic trend of decreasing ratio values with proximity to the pipeline, whilst being insensitive to soil background effects, intimating their transferability to heterogeneous field conditions. Field spectroradiometry data simulating the default channel settings for CASI-2, AVIRIS and programmable 725:702 and 723:700 nm wavelengths for CASI-2, AVIRIS and Eagle hyperspectral airborne sensors revealed that channel centre wavelength positions influenced the sensors ability to detect stress. The CASI-2 713:703 and 743:703 nm default channels were able to distinguish vegetation stress associated with pipeline earthworks to the same degree as the 725:702 and 723:700 nm wavelengths. Under operational conditions the CASI-2 743:703 nm ratio was also capable of detecting barley field stress identified by the original field spectra 723:700 and 725:702 nm ratios.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available