The measurement of plant water content is essential to assess stress and
disturbance in forest plantations. Traditional techniques to assess plant water
content are costly, time consuming and spatially restrictive. Remote sensing
techniques offer the alternative of a non destructive and instantaneous method of
assessing plant water content over large spatial scales where ground
measurements would be impossible on a regular basis. The aim of this research
was to assess the relationship between plant water content and reflectance data in
Eucalyptus grandis forest stands in KwaZulu-Natal, South Africa. Field reflectance
and first derivative reflectance data were correlated with plant water content. The
first derivative reflectance performed better than the field reflectance data in
estimating plant water content with high correlations in the visible and mid-infrared
portions of the electromagnetic spectrum. Several reflectance indices were also
tested to evaluate their effectiveness in estimating plant water content and were
compared to the red edge position. The red edge position calculated from the first
derivative reflectance and from the linear four-point interpolation method performed
better than all the water indices tested. It was therefore concluded that the red
edge position can be used in association with other water indices as a stable
spectral parameter to estimate plant water content on hyperspectral data. The
South African satellite SumbandilaSat is due for launch in the near future and it is
essential to test the utility of this satellite in estimating plant water content, a study
which has not been done before. The field reflectance data from this study was
resampled to the SumbandilaSat band settings and was put into a neural network
to test its potential in estimating plant water content. The integrated approach
involving neural networks and the resampled field spectral data successfully
predicted plant water content with a correlation coefficient of 0.74 and a root mean
square error (RMSE) of 1.41 on an independent test dataset outperforming the
traditional multiple regression method of estimation. The potential of the
SumbandilaSat wavebands to estimate plant water content was tested using a
sensitivity analysis. The results from the sensitivity analysis indicated that the xanthophyll, blue and near infrared wavebands are the three most important
wavebands used by the neural network in estimating plant water content. It was
therefore concluded that these three bands of the SumbandilaSat are essential for
plant water estimation. In general this study showed the potential of up-scaling field
spectral data to the SumbandilaSat, the second South African satellite scheduled
for launch in the near future. / Thesis (M.Sc.) - University of KwaZulu-Natal, Pietermaritzburg, 2008.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:ukzn/oai:http://researchspace.ukzn.ac.za:10413/263 |
| Date | January 2008 |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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