527 
I— * 
I CONTROL 
-I NOMENCLATURE TEST 
CLASSIFIEO IMAGE 
ATISFAÇTORV NOT.SATIS 
detection of the 
igital classifi- 
a supervised met 
ier . 
els around vege- 
Boundary pixels 
represented port- 
ypes. Their values 
area of each 
the relative ref- 
. This was also 
es, and this was 
gy, heterogeneous 
over. 
ion were checked 
with the digital 
etation was asses- 
as within the 
al-Cavarre and 
e 2) . 
[ood mapped by the 
SPOT-HRV) with a 
•T-simulation 
it 5 TM (treshold: 
idsat 5 TM-sensor 
vegetation cover 
ild: 3.001. H2 Dry 
for the SPOT-simu- 
reshold: 5.00), 
bad detected and 
itercontent and the 
:ypes of vegetation. 
?ed a very good 
ition (treshold: 
3.00) and 97 % for the Landsat 5 TM (treshold: 5.00) , 
respectively. 
3.4.3 Forests 
G7' Rich meadow with willow and birch showed a very 
good accuracy for the SPOT-simulation (accuracy of 
91.3 %). What G7 Birch forests (meadow type) concern, 
the accuracy was rather low. The same low percent of 
accuracy were shown by E5b Grey alder forests (poorer 
type), Bl Birch forests (richer heath type) and A4b 
Birch forests (shrub type) were rather bad mapped in 
the area of Dividal-Cavarre by the both systems (ac 
curacy up to 24 %). In the area of Saratr0a-Habafjell 
was this vegetation cover type mapped by a accuracy 
for SPOT-simulation of 87 % (treshold: 3.00) and 93 % 
for Landsat 5 TM (treshold: 3.00), respectively. 
E5a and E5c, Grey alder forests, were very good 
mapped by the both systems, with a accuracy of 94 % 
for SPOT (treshold: 3.00) and 88 % for Landsat 5 TM 
(treshold: 3.00), respectively. 
Table 1. ACCURACY OF THE DIGITAL CLASSIFICATION 
Dividal - Cavarre Accuracy in percent 
Covertype 
Treshold 
SPOT-sim 
3.0 4.0 5.0 
Landsat 5 TM 
3.0 4.0 5.0 
Vegetation- 
map 
(ha) 
A4b Birch 
forests 
7.6 7.8 
7.8 
7.0 7.6 7.8 
31.9 
(heath) 
23% 24% 
24% 
21% 23% 23% 
Bl Birch 
forests 
2.1 1.8 
1.6 
1.6 1.5 1.5 
6.4 
(richer heath) 
32% 28% 
26% 
25% 23% 23% 
E5a,c Grey Alder 
forests' (very rich 
4.8 5.5 
5.6 
5.2 5.9 5.9 
4.6 
type) 
94% 84% 
81 % 
88% 78% 78% 
E5b Grey Alder 
forests (poorer 
5.5 5.8 
5.6 
8.0 8.1 8.1 
1 .6 
type) 
29% 27% 
26% 
20% 19% 19% 
G7 Birch 
forests (meadow 
1.2 1.2 
1 .2 
11.2 11.6 11.9 
0.2 
type) 
16% 16% 
16% 
1% 0.7% 0.7% 
H1 Extremely 
24.0 28. 
0 29.8 
9.2 10.1 10.4 
15.8 
dry shrub 
66% 56% 53% 
58% 63% 66% 
H2 Dry 
14.4 16. 
2 17.6 
47.9 51.3 51.9 
27.6 
Shrub 
52% 59% 64% 
57% 53% 53% 
H7 Rich 
6.2 7.2 
7.2 
1.6 1.4 1.4 
1.8 
shrub 
29% 25% 
25% 
89% 78% 78% 
A1/A2 Farmland 
0.6 1.0 
32% 50% 
1 .7 
55% 
0.4 0.3 0.3 
20% 15% 15% 
2.0 
Unclassified pixels 
Snowcover 
23.6 13. 
9.6 11. 
6 10.6 
5 11.0 
7.4 1.7 0.3 
3.4.4 Farmland 
AA1/AA2 Farmland was very bad detected and mapped by 
both of the sensorsystems of SPOT-HRV and Landsat 5 TM, 
due to the early phenological stage and the high water- 
content in the soil. 
3.4.5 General comments and discussion 
The number of unclassified pixels were rather great, 
due to the early phenological stage with wide varia 
tion within the vegetation cover types and the distri 
bution of snowcover. 
The investigation has shown that the Landsat 5 TM- 
scene from the springtime almost had the same accuracy 
by supervised classification as the SPOT-simulated 
imagery, due to the better radiometric resolution for 
the TM-sensor compared to the simulated HRV-sensor on 
the SPOT-satellite, for mapping purposes. 
By comparing the classifcation results with the 
"ground truth" - digital map, the main trend was that 
Table 2. ACCURACY OF THE DIGITAL CLASSIFICATION 
Saratr0a - Habafjell Accuracy in percent 
Covertype 
Treshold 
SPOT 
3.0 
-sim. 
4.0 5. 
.0 
Landsat 5 TM 
3.0 4.0 5.0 
Vegetation- 
map 
(ha) 
Va Water 
2.4 
2.5 2. 
.6 
2.5 
2.6 2.7 
2.7 
(lakes and 
89% 
92% 96% 
92% 
96% 100% 
rivers) 
A4b Birch 
forests 
27.1 
35.0 
37.0 
25.6 
33.0 34.1 
23.8 
(heath) 
87% 
68% 
64% 
93% 
72% 69% 
G7 1 Rich meadow 
with willow and 
2.1 
2.1 
2.1 
2.3 
birch 
91 % 
91 % 
91 % 
H1 Extremely dry 
3.5 
3.3 
3.4 
3.0 
3.8 4.1 
4.4 
shrub 
89% 
76% 
78% 
68% 
86% 93% 
H2 Dry 
8.7 
10.0 
11.1 
11.1 
15.3 20.3 
30.7 
shrub 
28% 
34% 
36% 
36% 
49% 66% 
P2 Wet 
5.5 
6.8 
7.5 
0.4 
0.1 0.1 
3.3 
shrub 
62% 
48% 
44% 
12% 
3% 3% 
Q4 Poor mire 
15.2 
16.1 
17.3 
12.0 
11.6 12.4 
12.7 
(intermediate type) 
83% 
77% 
73% 
94% 
91% 97% 
Q5 Rich 
3.2 
3.7 
4.3 
0.7 
1.3 1.4 
2.0 
mire 
62% 
54% 
46% 
35% 
65% 70% 
Unclassified pixels 
33.4 
16.0 
10.8 
42.8 
30.6 22.5 
the vegetation cover types which were most phenologic 
al develloped, showed the best accuracy in the super 
vised classification. This is due to the very early 
phenological stage and the distribution of snowcover 
in the mountain areas. But even the classification of 
the vegetation cover types HI Extremely dry shrub and 
Q4 Poor mire (intermediate type) in the mountain area 
was successful, with an overall classification accura 
cy of 90 % or more. 
Classification of the SPOT-simulated imagery showed 
that the vegetation cover types within smallareas, were 
better detected and mapped due to the better spatial 
resolution compared to the TM-sensor on the Landsat 5 
TM satellite. 
The autumn-scene taken by the Landsat 5 TM-sensor 
could not be used as a basis for supervised digital 
classification, due to the the very low sunelevation 
and to the very late phenological stage. 
Several authors have discussed the possibilities of 
using SPOT- and Landsat-imagery as a basis for mapping 
of vegetation. Sadowski & Sarno (1976) stated the con 
ventional ML-classifier was too bad for mapping pur 
poses, and they used a contextual ML-classifier ins 
tead. The result of this was an improvement of the ac 
curacy for the classification, and this was also the 
trend for channels with a spatial resolution > 32 m. 
Teillfet et al. (1981) compared MSS-data with simula 
ted TM-data, and stated that the accuracy of classi 
fication was improved by using TM-data instead of MSS- 
data (MSS: 67 % of accuracy and TM: 83 % of accuracy) 
in classification of forest cover types. This improve 
ment was a result of the better radiometric and spatial 
resolution for the TM-sensor. In addition they found 
none significant improvement as a result of improve 
ment in spatial resolution alone. This is also my ex 
perience . 
Jaakkola (1985) stated that it is obvious that SPOT- 
data will benefit from the use of multi-point (contex 
tual methods) instead of single-point classifiers. He 
also stated that texture should be used in the classi 
fication. I will agree in this statement what SPOT-data 
concern, but for TM-data with the improved radiometric 
resolution, it is my meaning that multi-point classi 
fiers only can give marginal improvemant. Jaakkola (19 
85) also stated that forest cover type-classification 
into six classes was successful with an overall classi 
fication accuracy of 90 percent or more. For some of 
the forest cover types and even other vegetation cover 
types, I got a successful result with an overall clas 
sification accuracy of 90 percent or more. Especially