SPOT simulation 1982-06-30 Time: 12.32.42PM 
IR Imagery 1982-06-30 Time: 12.32.42PM 
BW Imagery 1975 Summer (July). 
2.4 Laboratory work 
Digital and visual classification were carried out at 
the I2S IMAGE PROCESSING SYSTEM (MODEL 575) at Troms0 
Telemetry Station (TTS). 
2.5 Classification 
The procedures followed are illustrated in the flow 
chart (figure 2). A supervised classification method 
has been elaborated using MAX-LOG-LIKELIHOOD classi- . 
fier with treshold of 3.00, 4.00 and 5.00, respective 
ly. In addition to this, several caanael combinations 
and ratios were used both for digital and visual 
classification. 
2.6 Accuracy assessment 
Then®are two major types of accuracy assessment prose- 
dures: 
1.Non-site-specific (total area extent) 
2.Site-specific (location). 
Non-site-specific accuracy is usually expressed as the 
similarity between the total numbers of hectares in 
each vegetation-cover type as determined by a Landsat 
or SPOT classification, compared to the corresponding 
total area determined from the digital vegetation map. 
The non-site-specific method compares only the total 
area without regard to location. Site-specific accu^ 
racy, however, considers the spatial nature of the da 
ta when two spatially defined data sets (one ground 
truth) are registred and compared for the amount of 
agreement (Reichert & Crown 1984). The chosen method 
in this study is the no-site-specific method. 
3.0 RESULTS AND DISCUSSION 
The results of the prosject is here in this chapter 
presented and discussed. The basic tabels and figures 
from the prosject is not presented here, but I refer 
to my thesis-report (T0mmervik 1985a). 
3.1 Floristic and Phytosociology 
The area is very varioues and rich what species con 
cern, and of the the 489 plant taxa at species and 
subspecies levels encountered in the ground truth pro 
gram, 371 were vascular plants, 82 mosses and 36 lich 
ens. The result of the phytosociological study which 
was carried out during this prosject, was that 39 main 
vegetation-units on several levels in the phytososio- 
logical hierarchy were picked out. 
3.2 Digital vegetation map 
A digital vegetation map has been generated on basis 
of the ground truth program, and the legend of this 
map consists of 31 vegetation cover types. The work 
with the digital vegetation map has shown that it is 
a good tool for resource studies and vegetation map 
ping purposes, and that it can be updated very easily. 
3.3 Digital image processing 
Digital image processing was done both on SPOT-simu- 
lated imagery and Landsat 5 TM imageries. Unfortuna 
tely both the spring-scenes from 1982 and 1984 were 
taken too early in the spring due to snowcover, and 
the autumn-scene was taken to late in the autumn to 
Figure 2. Methodology flow chart for detection of the 
vegetation cover types. 
give a sufficient basis for a good digital classifi 
cation . 
3.4 Digital classification 
Digital classification was based on a supervised met 
hod using MAX-LOG-LIKELIHOOD classifier. 
The resulting themes left many pixels around vege 
tation type boundaries unclassified. Boundary pixels 
presented a special problem, as they represented port 
ions of different vegetation cover types. Their values 
were a function of the amount of the area of each 
vegetation type within the pixel and the relative ref 
lectance of each material as a whole. This was also 
the result within the resulting themes, and this was 
a result of the variation in phenology, heterogeneous 
vegetation and distribution of snowcover. 
The interpretation and classification were checked 
by comparing the classified imagery with the digital 
map, and the accuracy of the interpretation was asses 
sed on a quantitative basis. Two areas within the 
digital map area were checked, Dividal-Cavarre and 
Saratr0a-Habafjell (Table 1 and Table 2). 
3.4.1 Alpine vegetation 
Hi Extremely dry shrub were rather good mapped by the 
two sensorsystems (Landsat 5 TM and SPOT-HRV) with a 
optimal accuracy of 89 % for the SPOT-simulation 
(treshold: 3.00) and 93 % for Landsat 5 TM (treshold: 
5.00). For H7 Rich shrub was the Landsat 5 TM-sensor 
the best sensor for mapping of this vegetation cover 
type with a accuracy of 89 % (treshold: 3.00). H2 Dry 
shrub was rather bad detected, 36 % for the SPOT-simu 
lation and 66 % for Landsat 5 TM (treshold: 5.00), 
respectively. 
3.4.2 Mire vegetation 
Q5 Rich mire and P2' Wet shrub were bad detected and 
mapped, due to the high amount of watercontent and the 
distribution of snowcover at these types of vegetation. 
But Q4 Poor mire (intermediate) showed a very good 
accuracy of 83 % for the SPOT-simulation (treshold: