values for each of SPOT’s three
spectral bands) to 64 habitat classes.
Preprocessing entails applying an
unsupervised maximum likelihood
clustering algorithm to the data thus
reducing the SPOT imagery from 8
bit to 6 bit. Reflectance values
cluster out within a three
dimensional array defined by
SPOT’s three spectral bands. The
algorithm reduces the data to 64
clusters and assigns each a unique
value. During the final pass, every
pixel is then given the value of the
cluster within which it is most likely to
fall (Lillesand and Kiefer 1987).
GAIA software served three major
purposes in this study: accessing
the satellite imagery; measuring
distance to mainland and to yhe
nearest larger island for each island;
and exporting the imagery as a PICT
II file. These exported PICT II images
were analyzed with Image software
package (Rasband 1990). I used
Image's particle analysis routines to
measure the perimeter and area of
each island as well as to tally the
islands’ spectral classes.
Explanation of Terms: Area.
Perimeter, and Convolutedness.
All data extracted from the imagery
was in pixel units and hence are
accurate to the 20 meter resolution of
the SPOT MS imagery. The SPOT
data used in this study was acquired
at high tide so all values for area and
perimeter represent only the
terrestrial portions of the islands.
In an effort to measure an island’s
convolutedness, I derived an index
to indicate the degree to which a
given island’s perimeter varies from
a circle’s of equal area. The shape
that yields the smallest perimeter for
a given area is a circle and can be
expressed:
P'=2t (1)
V 7t
where P’ is the smallest possible
perimeter and A is the island’s area.
The index of convolutedness (C) is
simply the ratio between an island’s
actual perimeter and its theoretical
least perimeter:
c-e. (2)
p’
where P is the island’s perimeter, P’
is its least possible perimeter and C
is the index of convolutedness. The
more irregular an island, the higher
its index of convolutedness.
Landscape Richness and Diversity
I used the total number of spectral
classes present on an island as a
measure of its landscape richness.
As mentioned above, I assumed that
an island with relatively high spectral
class richness were also islands with
high landscape or habitat richness.
I then inserted landscape richness
values into the Shannon-Weaver
index of diversity to derive an
island’s overall landscape diversity
(Shannon and Weaver 1949).
Typically this index is used to
measure "species" diversity (Peet
1975). Diversity (H) is calculated by
the equation:
H’ = - S Pi In pj (3)
where p/ is the proportion of species
/in a sample of s species. As the
number of species in a system
increases, especially if the relative
proportions of those species are
uniform, H will tend to be high. I
calculated landscape diversity for
each island of the 423 by substituting
pixel tallies of each spectral class for
species in the above model. In this
way H will tend to increase when the
number of spectral classes or
habitats on an island is high and the
proportions of those habitats are
uniform.
I calculated maximum theoretical
diversity (Hmax) for given habitat
richness (s) to isolate those islands
with uneven habitat distribution.
Maximum diversity is defined as the
Shannon-Weaver index resulting
from perfectly even distribution of
pixels in spectral classes. In such a
situation, all p/ are equal and:
where p equals the total number of
any given habitat class (because
they are uniformly represented) and
s is the total number of classes
present. Substituting this equation
into the Shannon-Weaver equation
leaves:
Hmax= • l n (^-)
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