yed many prominent
i to known surface
wed differences in
j Gum site quality 1
n flooded) appearinc
r (i.e. vegetation on
¡d the colour lighter,
adar appeared dark,
tigation of bispectral
ind Landsat images,
le range of radar
ses was developed
e cover classes
i >Dark
¡3
Swamp
Agriculture
water
ark as they acted as
jiving antennae. The
so the density of the
te quality (Forestry
e forest type (i.e. the
the radar response.
5), who found that
increasing age (or
(during the needle
iund that for L-Band
correlated with tree
Des of pine forest in
id higher backscatter
phenomena may be
i subject to an 80%
iis aspect is to be
verall classification
-B data classification
ng accuracy was nol
r the supervised
[¡cation accuracy
Table 5 - Mapping accuracies for the supervised classification of the
combined Landsat and SIR-B data.
Class A
VV
F
I
II
III
cr
“T -
TM
Agriculture (AJ 27
27
0
0
o
o
Swamp (S) 3
11
1
7
1
6
29
18
62
38
Water (W)
1
28
1
30
2
7
93
Box (B)
23
1
1
2
2
29
6
21
79
Red Gum SQ1 (I)
4
1
19
6
1
31
12
39
61
Red Gum SQ2 (II)
2
2
2
21
1
2
30
9
30
30
Red Gum SQ3 (III)
2
12
1
7
7
1
30
23
77
23
Total No. of
Pixels (T)30
20
30
44
24
36
10
12
209
No. Comissions 3
9
2
21
5
15
3
12
% Comissions 10
45
7
48
21
42
30
100
U = Unclassified pixels
OM = number of omissions
P = percentage of omissions
CA = class mapping accuracy
statistical testing of thematic map accuracy. Rem. Sens, of the
„ Environ. 7:3-14.
Wu S. (1984). Analysis of synthetic aperture radar data acquired ovei
a variety of land covers. IEEE Trans, on Geosci. and Rem. Sens.
GE-22(6):550-5578.
There was some qualitative’evidence to suggest that the
remote sensing data was more accurate than some, sections of the
site quality and vegetation maps used for ground truthing and
mapping accuracy assesment. A more detailed ground truthing
exercise is needed to evaluate whether some misclassified pixels ar<
actually correctly classified, and in fact it is the ground truth data
which is inaccurate.
Some research has been undertaken to determine the optimal
combination of wavelength, polarization, resolution and look angle
for agricultural applications (De Loor, 1974; Ulaby, 1975; Brakke el
al., 1981; Dobson et al., 1983), though much still needs to be done in
forestry. Reliable models to describe radar backscatter from forests
also need to be developed.
4. Conclusions
The highest overall classification accuracy of 65% was obtained
with co-registered Landsat MSS and SIR-B radar data. SIR-B provides
additional information for delineation of forest types and site
quality classes for the Riverina forests of Australia, though the
amount of extra information is limited. Stand structure appeared the
main factor affecting radar backscatter from forests.
Acknowledgements
Mr T. Lee provided assistance in running the static average
filter which he developed on the Dipix system, at the Centre tor
Remote Sensing, Unversity of New South Wales. Ms L. Bischof
provided valuable advice on the operation (and peculiarities) of the
Dipix system.
We are also grateful to The Forestry Commission of N.S.W. and
the Department of Conservation, Forests and Lands, Victoria, who
made available ground truth maps and reports of the study area.
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502%
56.4%
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itributed information
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