Remote sensing of grassland with contaminated soil using the spectral red-edge
Remote sensing of grassland with contaminated soil using the spectral red-edge
In most cases contaminants are concealed in soil and under vegetation and therefore can
not be measured directly by remote sensing. However, soil contaminants were detected
using the spectral red-edge to indicate vegetation stress caused by the presence of
the contaminants. An improved red-edge position (REP) was developed and gave a
slight improvement in the predictive capability over existing indices and an effective
additional diagnostic indicator of soil contamination was found to be the spatial pattern
of the REP. Where an area had high levels of hydrocarbon in the soil it also had a
high level of variation. The indication was that spatial variation of spectral indices
(especially the REP) may be more useful than the spectral index value for the detection
and mapping of soil contamination.
Field analysis and radiative transfer modelling (using a coupled leaf and canopy model,
LIBSAIL) showed the influence of vertical layering in the grassland canopy. The influence
of a vegetated under-storey on the red-edge was found to be greatest when
different absorption spectra were present and high within-the-leaf scattering. The former
defined wavelength positions of features while the later determined if they were
resolvable in a spectrum. This greater understanding of the grassland canopy identified
the importance of fully surveying vegetation canopy structure, especially in complex,
multi-layered canopies such as those found with contamination. With this understanding
of what the red-edge can reveal, remote sensing is an effective tool for the detection
of contamination.
Llewellyn, Gary Michael
55128ee9-20f8-424f-80f3-80e206e75a66
June 2009
Llewellyn, Gary Michael
55128ee9-20f8-424f-80f3-80e206e75a66
Curran, Paul
f4fb9ba5-0432-48a5-a351-9d75536458ee
Milton, Edward J.
f6cb5c0d-a5d4-47d7-860f-096de08e0c24
Llewellyn, Gary Michael
(2009)
Remote sensing of grassland with contaminated soil using the spectral red-edge.
University of Southampton, School of Geography, Doctoral Thesis, 474pp.
Record type:
Thesis
(Doctoral)
Abstract
In most cases contaminants are concealed in soil and under vegetation and therefore can
not be measured directly by remote sensing. However, soil contaminants were detected
using the spectral red-edge to indicate vegetation stress caused by the presence of
the contaminants. An improved red-edge position (REP) was developed and gave a
slight improvement in the predictive capability over existing indices and an effective
additional diagnostic indicator of soil contamination was found to be the spatial pattern
of the REP. Where an area had high levels of hydrocarbon in the soil it also had a
high level of variation. The indication was that spatial variation of spectral indices
(especially the REP) may be more useful than the spectral index value for the detection
and mapping of soil contamination.
Field analysis and radiative transfer modelling (using a coupled leaf and canopy model,
LIBSAIL) showed the influence of vertical layering in the grassland canopy. The influence
of a vegetated under-storey on the red-edge was found to be greatest when
different absorption spectra were present and high within-the-leaf scattering. The former
defined wavelength positions of features while the later determined if they were
resolvable in a spectrum. This greater understanding of the grassland canopy identified
the importance of fully surveying vegetation canopy structure, especially in complex,
multi-layered canopies such as those found with contamination. With this understanding
of what the red-edge can reveal, remote sensing is an effective tool for the detection
of contamination.
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LLEWELLYN_final.pdf
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Published date: June 2009
Organisations:
University of Southampton
Identifiers
Local EPrints ID: 160117
URI: http://eprints.soton.ac.uk/id/eprint/160117
PURE UUID: f8b04bdc-6cf0-47de-ae36-2f0ba5380886
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Date deposited: 16 Jul 2010 11:13
Last modified: 14 Mar 2024 01:56
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Contributors
Author:
Gary Michael Llewellyn
Thesis advisor:
Paul Curran
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