lission 
on Correct 
Total % 
141 
26 
50 
27 
139 
73 
45 
69 
104 
74 
192 
74 
183 
64 
149 
82 
85 
69 
11 
6 
207 
89 
185 
48 
119 
79 
91 
87 
277 
85 
705 
93 
295 
41 
/ 
+ 
I 
V 
X 
/ 
/ 
nd 3 
I9-0'89(u»w) 
:he sixteen 
From inspection of Fig 1, one would also 
expect misclassification between the 
spectrally-similar values of: European Larch 
(1933) and (1949); Oak (1845) and (1915); 
Douglas Fir (1966) and Norway Spruce/Scots 
Pine (1966); and finally Corsican Pine 
(1965/6) and Norway Spruce (1940). This is 
borne out by reference to the confusion 
matrix. The classification of Norway Spruce 
(1940) would at first seem abnormal with a 
classification accuracy of just 6%. In fact 
93% of the training area has been 
misclassified as Corsican Pine. A possible 
explanation could be the result of the close 
juxtaposition between the spectral values 
(See Fig 1) but the higher variance in all 
three bands of the Corsican Pine (See Table 
2) , such that in the classification process 
most pixels are classified as the latter. 
At this stage it was decided to merge the 
classes Oak (1845) and Oak (1915) . By 
treating the two as one class, the 
classification accuracy increases as shown at 
the foot of Table 1. The European/Hybrid 
Larch (1981) class would be more 
appropriately labelled 'clearcut' as the very 
young, well-spaced trees would contribute a 
negligible proportion of reflectance within a 
single pixel. 
Table 2. Standard deviations about mean for 
the 16 classes. 
Band 1 Band 2 Band 3 
Oak 
1845 
2.1 
1.7 
0.4 
If 
1915 
2.2 
1.4 
0.5 
II 
1947/9 
3.4 
2.7 
0.4 
SwCh 
1961 
1.9 
1.1 
0.4 
EL 
1933 
1.9 
1.0 
0.4 
" 
1949 
2.6 
1.0 
0.4 
EL/HL 
1981 
5.4 
1.7 
0.5 
HL 
1971 
2.4 
0.7 
0.3 
DF 
1966 
2.1 
1.4 
0.5 
NS 
1940 
1.7 
1.1 
0.3 
If 
1971/2 
2.8 
0.9 
0.4 
NS/SP 
1966 
1.9 
1.0 
0.4 
SP 
1928 
2.2 
0.9 
0.4 
CP 
1965/6 
3.1 
2.5 
0.4 
URBAN 
10 
8.7 
0.8 
AGRIC 
8.2 
5.3 
0.6 
Table 3. 
Summary 
of 
Commis s 
ion/Oruission 
Errors for Evaluation Areas. 
Class 
Omission 
Errors 
Total % 
Commission 
Errors 
Total % 
Correct 
Total % 
Oak 
1845/ 
1915 
218 
81 
15 
23 
51 
19 
" 
1947/9 
72 
61 
89 
65 
47 
39 
SW CH 
1961 
88 
100 
3 
100 
0 
0 
E L 
1933 
170 
81 
11 
22 
40 
19 
** 
1949 
50 
51 
33 
41 
48 
49 
EL/HL 
1981 
34 
55 
7 
20 
28 
45 
H L 
1971 
67 
66 
3 
8 
34 
34 
D F 
1966 
84 
47 
67 
41 
96 
53 
N S 
1940 
101 
99 
0 
0 
1 
1 
” 
1971/2 
96 
55 
8 
9 
79 
45 
NS/SP 
1966 
150 
69 
17 
20 
67 
31 
S P 
1928 
42 
51 
4 
9 
41 
49 
C P 
1965/6 
24 
21 
200 
68 
92 
79 
URBAN 
6 
4 
641 
81 
149 
96 
AGRIC 
69 
15 
1 
0 
390 
85 
A more credible accuracy assessment is 
based upon sites of known identity not used 
in the training procedure. These were the 
evaluation areas, and a summary of the errors 
is listed in Table 3. As expected, the 
overall accuracies have decreased though once 
again misclassification of urban is the main 
source of error. The total percentage of 
pixels correctly classified is 42.9%. A 
smoothing filter applied to the 
classification slightly increased this to 
43.6%. One would normally expect a greater 
increase in accuracy than this, since the 
smoothing filter eliminates stray, erronously 
classified pixels within a homogenous stand 
of trees. However, where certain stands have 
been predominatly classified as urban, this 
misclassification only becomes magnified. 
The major problem to be addressed is the high 
misclassification errors resulting from the 
highly textured urban class. Simply omitting 
this class form the training data would only 
result in the misclassification of urban 
areas as woodland. The drawback with the 
classification algorithm is that it is based 
on the spectral analysis of pixels purely on 
an individual basis. Work is currently 
underway in analysing the effect of applying 
a textural classifier to the imagery. This 
method has been shown to result in increased 
classification accuracies in highly textured 
regions. (Isako, 1979) .The texture band is 
created by using a 'split and merge' 
technique developed by Cross and Mason 
(1985) . Preliminary results suggest a 
significant increase in classification 
accuracy. An alternative approach would be to 
apply a 'layered' classification. By using 
vegetation indices, vegetated and 
non-vegetated regions are separated early in 
the classification, preventing confusion 
later in the classification process. 
However, if errors are made at the first 
stage they are carried on to lower levels, 
regardless of the soundness of the later 
decisions. If the urban areas can be 
sufficiently well recognized in the enhanced 
false colour composite, it is also possible 
that they could be simply 'masked' from the 
classification. 
Given the hypothetical situation whereby 
all the pixels misclassified as urban are 
correctly classified, there is a drastic 
increase in the proportions correctly 
classified (See Table 4) . The effect of the 
smoothing filter is also shown. 
With reference back to Fig 1, the 
distribution of the spectral curves appears 
to agree with the findings of Mayer and Fox 
III (1981), and later Kachhwaha (1983) . 
Mayer and Fox concluded that band 5,6 and 7 
Landsat MSS digital numbers have strong 
correlation to the size and density of the 
(coniferous) trees. High digital numbers 
tended to be indicative of poor stocking in 
band 5 (similar to SPOT band 2) and younger 
trees in band 7 (closest to SPOT band 3) . 
From Fig 1, the well-stocked stands of Norway 
Spruce, Norway Spruce/Scots Pine, Corsican 
Pine and Douglas Fir all have very similar 
reflectance in band 2, but the younger stands 
of Douglas Fir and Norway Spruce/Scots Pine 
have a much higher reflectance in band 3. An 
outlier is Norway Spruce (1971/2). According 
to Mayer and Fox, this is typical for young 
plantations of small numerous trees. One 
might expect a lower overall band 2 digital 
value due to the high tree density. However, 
the small crown size and high percentage of 
exposed bare soil probably causes the high 
digital counts.