RESEARCH FOR

SOME PECULIARITIES OF LABORATORY MEASURED
HYPERSPECTRAL REFLECTANCE CHARACTERISTICS
OF SCOTS PINE AND NORWAY SPRUCE NEEDLES
information from the data matrix and to represent it
as a set of new orthogonal variables called principal
components (Čekanavičius and Murauskas, 2002). In
this study all 108 measurements of tree samples were
averaged to represent only each single tree selected
for research. This was considered to serve for more
convenient interpretation of results of the analysis.
That is, data of 6 spectral curves (3 for spruce and 3
for pine) were loaded to principal component analysis.
The results of principal component analysis are
presented in Figure 4. This is a two-dimensional scatter
plot of scores of two most important components
from analysis, where X axis represents first principal
component, but Y axis represents second principal
component.
Figure 4. Scores of principal component (PC)
analysis for Scots pine (SP) and Norway spruce (NS).
The plot provides information about patterns in
the samples. The closer the samples are in the scores
plot, the more similar they are with respect to the
two components concerned. Conversely, samples
far away from each other are different from each
other. The plot can be used to interpret differences
and similarities among samples. The plot proves that
Scots pine and Norway spruce are spectrally separable
from each other. General separability trend between
pine and spruce is best described by the first principal
component. The dots representing Scots pine tend to
form a group while dots representing Norway spruce
tend to spread. Thus, spectral similarity among separate
Scots pine sample trees appears to be relatively good
while spectral similarity among Norway spruce trees
- relatively poor. The results of ANOVA test validate
this conclusion.
Gediminas Masaitis, Gintautas Mozgeris
The use of in situ spectral measurements for
species discrimination is often employed at both
laboratory level or field level (Adam and Mutanga,
2009; Castro-Esau et al., 2004; Cochrane, 2000;
Hesketh and Sánchez-Azofeifa, 2012; Kalacska et
al., 2007; Manakos, 2003; Manevski et al., 2011;
Vaiphasa et al., 2005; Zhang et al., 2006). Every
study combines its own approaches to the statistical
treatment of reflectance data. Apparently, there is no
universal method for the optimal feature selection in
hyperspectral data treatment.
In this study no effort to decrease data redundancy
between adjacent bands was made. Our goal was to use
the full possible range of data of all narrow wavebands
recorded by the hyperspectral camera. Thus, we were
sure not to lose any important information that single
adjacent band could contain to the researched object.
conclusions
This study revealed that:
1. A significant variation in spectral response of
needles of Norway spruce exists across whole
measured spectral range (955 wavebands) for each
sample tree.
2. A significant variation in spectral response of
needles of Scots pine exists only in 356 of 955
recorded wavebands for each sample tree. The
significant variance of spectral signatures among
single trees, especially of Norway spruce, suggests
expanding the field of research seeking to motivate
such variation. The research could involve a wider
sample trees selection including different sites,
tree age and growth conditions.
3. Depending on the aspect of a tree crown, the
reflectance of Scots pine needles differed
significantly in 900 out of 955 measured spectral
bands. This variance of reflectance might be
explained by the light demanding nature of Scots
pine.
4. The Scots pine and Norway spruce spectral
measurements proved that the discrimination of
these two species is possible at the laboratory level
hyperspectral imaging. This is a good prerequisite
to use airborne hyperspectral sensor for future
investigations of the potential of hyperspectral
imaging in application in forest inventory of
Lithuania.
references
1. Abd-Elrahman A., Croxton M., Pande-Chettri R., Toor G.S., Smith S., Hill J. (2011) In situ estimation
of water quality parameters in freshwater aquaculture ponds using hyperspectral imaging system. ISPRS
Journal of Photogrammetry and Remote Sensing, 66, pp. 463–472.
2. Adam E., Mutanga O. (2009) Spectral discrimination of papyrus vegetation (Cyperus papyrus L.) in
swamp wetlands using field spectrometry. ISPRS Journal of Photogrammetry and Remote Sensing, 64,
pp. 612–620.
30 ReseaRch foR RuRal Development 2012