426 
2.2 Image acquisition 
The study site was flown on 13th May 1984 as 
part of the UK SPOT Simulation Campaign, and 
the data was received some months later in 
the form of a computer-compatible tape. The 
20 m resolution multispectral digital data 
was acquired, but not the 10 m resolution 
panchromatic data. 
2.3 Analysis techniques 
Computer-assisted interpretation of the image 
data was carried out using the GEMS image 
processing system at the National Remote 
Sensing Centre, Farnborough, and at Silsoe 
College, Bedford. A manual contrast stretch 
was interactively selected to give the best 
visual definition and separation of tree 
types within the forest. A maximum 
likelihood classification was then applied to 
the resulting enhanced image. The training 
areas were delineated using 1:10,000 
Forestry Commission stock maps which provide 
information on species composition, exact 
location and date of planting. 
At least two sets of training areas were 
compiled for each class within the 
classification. The first set, called 
'training' data, were used to develop the 
statistics for the classification algorithm, 
and were also used for a preliminary accuracy 
assessment. The second set of training 
areas, called the 'evaluation' data, were 
used as an independent assessment of the 
classification accuracy. 
Based upon a visual inspection of the 
original enhanced image, it was decided to 
proceed with the selection of training areas 
at Anderson level III. No attempt was made 
at this stage to use ratio, principal 
component or texture algorithms in the 
classification. 
3 RESULTS AND DISCUSSION 
The final supervised classification used 16 
interpreter-defined classes. The simplest 
form of accuracy measurement is to compare 
the classified data with the training data 
used to generate the classification. This is 
presented in the form of a confusion matrix 
(see Dury et al 1986) . Inspection of the 
confusion matrix reveals the kinds of errors 
generated by the classification process 
(Table 1). The spectral curve patterns (Fig 
1) and standard deviations (Table 2) for each 
class also help to explain errors. 
The most: common errors of omission are the 
assignment of areas of forest to the urban 
category. All but one species, Norway 
Spruce (planted 1971) have been misclassified 
as urban. Both Oak (1845) and Oak (1915) 
have a massive 60% of the training area 
misclassified as urban. However the younger 
stands of Oak (1947/9) were not affected to 
the same extent. This over-classification of 
the urban class is attributable to the high 
degree of spectral variance within the class 
(compare the standard deviations for the 
urban class in Table 2 with the other 
classes) . It is interesting to note that 
although the agricultural class has high 
spectral variance, it has a very high mean in 
Band 3, the near infra-red, causing it to 
remain spectrally distinct from other 
classes. 
Table 1. Summary of Commission/Omission 
Errors 
Class 
Omission 
Errors 
Total % 
Commission 
Errors 
Total % 
Correct 
Total 
% 
Oak 
1845 
399 
74 
29 
17 
141 
26 
If 
1915 
132 
73 
84 
63 
50 
27 
If 
1947/9 
52 
27 
63 
31 
139 
73 
SW CH 
1961 
20 
31 
10 
18 
45 
69 
E L 
1933 
37 
26 
24 
19 
104 
74 
ft 
1949 
67 
26 
33 
15 
192 
74 
EL/HL 
1981 
104 
36 
9 
5 
183 
64 
H L 
1971 
33 
18 
5 
3 
149 
82 
D F 
1966 
38 
31 
134 
61 
85 
69 
N S 
1940 
187 
94 
2 
18 
11 
6 
ft 
1971/2 
26 
11 
0 
0 
207 
89 
NS/SP 
1966 
197 
52 
23 
11 
185 
48 
S P 
1928 
32 
21 
11 
8 
119 
79 
C P 
1965/6 
13 
13 
249 
73 
91 
87 
URBAN 
48 
15 
641 
70 
277 
85 
AGRIC 
42 
7 
33 
4 
705 
93 
Oak 
1845 & 
1915 
427 
59 
9 
3 
295 
41 
Fig 1. Spectral curves for the sixteen 
cover-types.