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Remote sensing for resources development and environmental management
Damen, M. C. J.

Lngh et al.
;s from the
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. An average
iza, 1975)
luation (7).
manner would
) and (7)
of L S (A) is
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is value of
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parallel to
would be
cedure is
found to be
value. The
) with radi
al number.
using ten
ee or four
'nly one case
iquired for
were perfect
Lon index (VI)
retaining the
a of
ar residual
igle, varying
1 data are
k of Duggin
1985, 1986)
factors which
w angle
the larger
e the larger
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ly sensed
tian surfaces
e as seen by
t necessarily
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made). An
me which has
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(1985, 1986)
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d by (a) above
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ises (b) and
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ition were
)84 at the
ring station,
a from about
i part
lose pixels
3 were positive.
3, and to some
extent identifies cloudy pixels over the land. Since
the data acquisition time was local afternoon and
since there were clouds on the western side of the
selected scene, there might have been some pixels
which were contaminated by cloud shadows and identi
fication of such pixels is not yet possible.
Raw NDVI were calculated using equation (1). The
atmospheric correction procedure was implemented and
spectral reflectances p(A^)and p(^2) were calculated.
Equation (9) then yields atmospherically corrected
NDVI values. A relation of the form of equation
(10) was sought between raw and atmospherically
corrected NDVI values.
Y = mX + c
where Y is atmospherically corrected NDVI and X
stands for raw NDVI. Line by line regression
analysis was performed on a 512 x 512 scene but
because of cloud cover and surrounding waters only
about 80 to 200 pixels per scanline corresponded to
cloud-free land area. For each scanline raw NDVIs
were highly correlated to atmospherically corrected
NDVI values. In fact the squared correlation
coefficient ranged from 85% to 98%. The parameter c
in equation (10) varied from about -0.1 to about
-0.03 whereas the slope (or enhancement or magni
fication) m ranged from about 2.2 to 3.5. These
results indicate that the relation (10) is not a
unique one. Had it been a unique relation then it
would have been of great value. Therefore, it
suggests that one has to apply atmospheric correction
to each scene of interest. It is also clear from the
values of m found above that the atmospherically
corrected NDVI imagery should have high contrast
compared to the contrast present in raw NDVI maps.
Next a relation similar to equation (10) was sought
between p (Aq) and atmospherically corrected NDVI.
There was a large variability in the value of m (2 to
30) but an important outcome was that the squared
correlation coefficient ranged from about 30% to
about 90%. This shows that p(Aq) carries some
extra information to which NDVI is not sensitive.
A similar analysis between p(A2) and atmospherically
corrected NDVI showed a poor correlation between
these two parameters. Therefore, p(Aq), p(A2) and
atmospherically corrected NDVI values may be useful
in improving surface cover type classification and
further investigations are underway.
The primary motivation was to search for more than
one parameter for land-cover classification.
Application of atmospheric correction results in an
increased contrast between too dissimilar surfaces.
It is shown that the atmospherically corrected NDVIs
are partially correlated to either channel reflect
ivity. This means that these three parameters may
prove to be of importance in improving land cover
classification. Although atmospherically corrected
NDVIs and raw NDVIs are highly correlated to each
other, these preliminary results indicate that there
is no unique relation between these two quantities.
Thus, one has to apply atmospheric correction to each
scene of interest.
This work was carried out under the NERC contract
No. F60/G6/12.
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