RESEARCH FOR

Gediminas Masaitis, Gintautas Mozgeris
species. It is a light demanding tree. Hence chemical
and structural characteristics of pine needles more
exposed to the sun differ from those which are longer
in the shade. Norway spruce is a shade tolerant tree. Its
north and south exposed needles spectral comparison
shows relatively low reflectance variation compared
to Scots pine.
ANOVA test was conducted to evaluate the
spectral variability inside the same tree species at
every spectral band. ANOVA provides a statistical
test if the means of several groups are different; thus,
ANOVA generalizes Student’s t-test for more than
two groups (Čekanavičius and Murauskas, 2002). In
our case ANOVA was conducted for three groups of
measurements (for 3 trees) for Norway spruce and
Scots pine respectively (Figure 3). Results of 955
ANOVA tests indicated that for spruce the hypothesis
that there is a significant variation among every single
tree at every spectral band cannot be rejected. For Scots
pine, ANOVA test showed a slightly different results.
Only 356 spectral bands of 955 were significantly
different among separate Scots pine trees. Those bands
were distributed into 3 separate ranges: 400-446.2 nm,
523.5 – 637.1 nm and 693.3 – 752.0 nm.
Discriminant analysis may be used for two
objectives: either to set the attributes which best
contribute to the separation of the groups of objects
under study (in our case tree species), or to assign
ReseaRch foR RuRal Development 2012
SOME PECULIARITIES OF LABORATORY MEASURED
HYPERSPECTRAL REFLECTANCE CHARACTERISTICS
OF SCOTS PINE AND NORWAY SPRUCE NEEDLES
Figure 2. Results of Student’s t-test (range of p-values) for Scots pine (a) and Norway spruce
(b) spectral separability test depending on needle aspect in the crown.
objects to one of a number of known groups of objects.
Thus, discriminant analysis may have a descriptive or
a predictive objective (Čekanavičius and Murauskas,
2002). Discriminant analysis was conducted as an
alternative and more convenient way to discriminate
between groups than Student’s t-test. Moreover,
the discriminant analysis can deal with more than 2
groups, unlike t-test. U statistic and F statistic were
calculated for every waveband (totally 955). Then
the bands were ranked according to the value of U
statistic in ascending order first, then according to the
value of F statistic in descending order. As a result,
the waveband which best separates two groups (pine
and spruce) was ranked at top. As it was expected, the
results are identical to the ones received by the t-test.
Best discriminating waveband was set at 667.1 nm,
best discriminating spectral range 666.5 nm –
668.4 nm (Figure 1, b). Discriminant analysis reveals
its full potential in classification tasks. That’s why
absolute majority of studies dealing with forest
hyperspectral imagery employ discriminant analysis
for predictive objectives in images classification
(Clark et al., 2005; Dalponte et al., 2009; Thenkabail
et al., 2004).
Principal component analysis is a method of an
analysis of a data matrix consisting of inter-correlated
quantitative dependent variables. Principal component
analysis is the way to extract the most important
Figure 3. Results of ANOVA (range of p-values) for Scots pine (a) and Norway spruce (b) for spectral
variability among separate sample trees.
29