865
verage of Xishuangbanna is illustrated
in Figure 1.
All processing has been executed on a
I 2 S model 75 with S600 software, linked
to a MicroVax as a host computer.
Band 1 (blue wavelength) was found to
be totally corrupted upon receipt and
was dropped from further analysis.
The TM image was subjected to a
radiometric standardisation whereby
digital numbers were converted to
radiances using the method proposed by
Price (1987). This standardisation is
in fact not necessary for the type of
exercise described here, but was
conducted anyway since the TM image was
also part of another exercise
involving multitemporal analysis in
conjunction with a Landsat MSS image.
The geometric correction involved a
projection to the UTM coordinate system
using a cubic convolution procedure.
From the complete frame , a subimage of
805 x 515 pixels, containing the
Mengyang Nature Reserve was extracted.
3.3. Experimental set-up
3.3.1. Vegetation classes
The total amount of pixels that were
identified in the field was divided at
random in a subset for supervised clas
sification training and a subset for
classification accuracy testing.
The list of training classes, and their
respective amount of pixels is shown in
Table 1.
The individual classes were further
aggregated to form a multilevel land
cover classification system (Sabins
1987) .
3.3.2. Classification strategies
Since the characteristics of the images
and the areas to be classified vary
greatly, it is not possible to put
forward a certain classifier that will
yield the best results (Campbell 1987).
Hence alternative strategies have to be
tested.
The experimental set-up of this
exercise contained several variables.
The single-step classification was
tested against a 'decision-tree'
(Mather 1987) or 'layered clas
sification'. This classification method
has been found to be cost-effective in
terms of CPU time, while yielding re
sults similar to the single-step clas
sifier (Lee and Richards 1985).
The layered classifiers were divided
into two types. In the first type the
hierarchical structure was based on
spectral subsets. In this case the
tree structure was determined by
visual inspection of graphs depicting
mean radiance ± 1 standard deviation
for every channel and every class .
The second type had a fixed structure,
based on the multilevel structure pre
sented in Table 1. This corresponds
with an information class hierarchical
structure.
Another experimental variable consisted
of using all available TM channels vs.
using only the SPOT-l-equivalent chan
nels 2, 3 and 4.
Furthermore, the use of single channels
was tested against the use of the best
subset of channels determined from the
use of the average pairwise divergence
as a separability measure (Swain 1978).
Since almost all classes involved ve
getation types, the use of the nor
malized difference (ND) (Rouse et al. ,
cit. Bariou et al. 1985) at the first
Table 1 List of training classes (with amount of training pixels between brackets)
Class Level III
Class Level II
Class Level I
1. tea (266)
2. rubber (233)
3. rubber/tea mixture (126)
4. citrus (7)
5. rice (155)
6. mixed agriculture (109)
7. recent shifting cultivation (43)
8. weeds (Eupatoriinn sp.) (23)
9. alpine meadows (140)
1. plantation agriculture
(632)
2. early crops (330)
1. agriculture (1102)
3. grasslands (140)
10. secondary forest (40)
11. secondary forest
(with Lithocarpus sp.) (10)
4. secondary forest (146)
12. forest with dominant bamboo (122)
13. scrub with dominant bamboo (220)
5. vegetation with
bamboo (342)
2. forest (1800)
14. monsoon forest (1178) I 6. primary forest (1352)
15. tropical rain forest (174)
16. fishing pond (14)
17. river (33)
7. water (47) | 3. water (47)