433 
200 
250 
nd 7 
200 
250 
and 8 
uce and black 
en healthy and 
dependent on 
is of green, 
aduced varying 
Figure 5. Scattergram outlines of MEIS-II bands 5 and 7 
for five classes at 10 m resolution. 
results. The unsupervised maximum likelihood classi 
fication gave very poor results and therefore cannot 
be used to classify budworm infestation. The super 
vised maximum likelihood classification gave slightly 
better results, but the results were dependent on the 
purity of the training areas and bands used. The 
same bands used to visually separate the healthy and 
budworm infested spruce again gave the better 
classification results. Confusion occurred between 
healthy spruce, budworm infested spruce, black 
spruce, and treed bogs (Tables 4 and 5). Edge pixels 
around the bogs created the greatest confusion. This 
classifier was found to be suitable for mapping bud 
worm infestation but had to be restricted to the 
drier areas where the least confusion occurred. 
Table 4. Classification similarities of 5 classes 
using 3 
MEIS-II 
bands at 
5.5 m 
resolution. 
Class 
BUDW 
HEAL 
ASPN 
BLSP 
WATR 
% scene 
BUDW 
322 
5 
30 
5 
1 
41.3 
HEAL 
19 
107 
0 
64 
1 
6.9 
ASPN 
14 
0 
1023 
0 
0 
27.5 
BLSP 
0 
18 
0 
353 
0 
5.8 
WATR 
0 
0 
0 
0 
2308 
1.3 
Total % 
90 
82 
97 
83 
98 
Table 
using 
5. Classification similarities of 5 
7 MEIS-II bands at 5.5 m resolution. 
classes 
Class 
BUDW 
HEAL 
ASPN 
BLSP 
WATR 
% scene 
BUDW 
342 
4 
0 
5 
0 
24.8 
HEAL 
13 
116 
0 
28 
0 
2.7 
ASPN 
0 
0 
1047 
0 
0 
13.6 
BLSP 
0 
10 
0 
389 
0 
3.9 
WATR 
0 
0 
0 
0 
2300 
1.2 
Total 
% 96 
89 
99 
92 
98 
The third, real-time parallelipiped classifier gave 
the best results with the least confusion in the 
drier areas of the scene. However confusion still 
occurred between the budworm infested spruce, black 
spruce and treed bogs. Because individual pixels can 
be used for training areas, specific reflectance 
values can be chosen for classification. Again, the 
number and choice of bands determined the amount of 
confusion between classes. Two appropriate bands 
quite often gave a good classification with the least 
confusion between classes. By adding a third band 
confusion between classes increased. This classifier 
can therefore be used to classify and separate 
healthy white spruce from budworm infested white 
spruce at the severe infestation level. There were 
some indications that a second level of infestation 
could be classified but with little accuracy and more 
confusion with other vegetation types. 
TM data 
A combination of band 1 (red), band 2 (green), and 
band 3 (blue) which gave a natural colour image with 
spruce budworm infested areas appearing reddish-brown 
was useful for visual analysis. Some confusion 
occurred between budworm infested spruce, black 
spruce, treed bogs, and healthy white spruce. A 
second combination which provided good visual sépara 
tion was band 3 (red) , band 4 (green) and band 5 
(blue). However, some confusion occurred between the 
budworm infested areas, wetland areas, black spruce, 
and healthy white spruce (Figure 6). Visual analysis 
of enhanced TM data can therefore be used for 
detecting spruce budworm infestation but with greater 
difficulty. 
Principal component and Martin Taylor enhancements 
did not improve upon the contrast stretches and were 
of little use. 
The unsupervised maximum likelihood classification 
gave very poor results and can therefore be discarded 
as a way of mapping budworm infestation. The super 
vised maximum likelihood classifier gave better 
results depending on the purity of the training areas 
used. The main areas of confusion were between bud 
worm infestation and the wetland areas. There was 
also confusion with black spruce (Table 6). In drier 
areas a greater accuracy of classification relative 
to the air photos was obtained because there is less 
confusion with other vegetation types. 
The real-time parallelipiped classifier gave the 
best results with less confusion between vegetation 
types. At the same time there was confusion between 
the budworm infested spruce, the wetland areas, black 
spruce and some healthy white spruce but only the 
severe infestation could be classified. To some 
extent, this method could therefore be used to map 
budworm infestation, but should be limited to drier 
areas where the least confusion occurred. 
Biomass indices obtained for a number of band com 
binations were displayed on the colour monitor for 
visual interpretation. The best combination was the 
biomass index of bands 7 and 3 (red), bands 5 and 3 
(green), and bands 4 and 1 (blue). Various combina 
tions of biomass indices and contrast stretched bands 
were also displayed, but gave no improvements in 
separation over straight biomass indices or contrast 
stretches. Separation of white spruce, both healthy 
and budworm infested, from other vegetation types was 
easily determined. Separation between healthy white 
spruce and budworm infested white spruce, on the 
other hand, was difficult. 
Table 6. Classification similarities of 5 classes 
using 3 TM bands at 10 m resolution. 
Class 
BUDW 
HEAL 
ASPN 
BLSP 
WATR 
% scene 
BUDW 
719 
1 
0 
19 
0 
25.8 
HEAL 
0 
766 
0 
0 
0 
9.0 
ASPN 
0 
0 
1108 
1 
0 
25.6 
BLSP 
82 
0 
0 
219 
4 
7.9 
WATR 
0 
0 
0 
0 
918 
1.5 
Total % 
89 
98 
100 
91 
99