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Remote sensing for resources development and environmental management
Damen, M. C. J.

forest cover types that were in a optimal phenological
stage (summer) got such a good classification accuracy.
Lannelongue & Saint (1981) investigated simulated
SPOT-data, and stated that "the geometry is not repre
sentative of the SPOT-system". I do hope that the real
SPOT-data will give us a possibility of improved detec
tion and mapping of forest cover types, but I don't
expect the optimal use of the SPOT-satellite system for
vegetation mapping purposes before the HRV-sensor get
an additional channel in the MIR-area of 1.5 - 1.7 um.
This is also the area where the vegetation cover types
with lichen cover is best detectable and mapped.
3.5 Visual interpretation and classification
Visual classification and interpretation was done on
the imageries. Ratio methods were elaborated using
channel-ratios of 5:3, 4:3, 5:4:3:2 and 4:3:2:1. In
addition to this, several channel-combinations were
interpretated in order to detect very hardly detectab
le vegetation cover types by digital classification.
The most successfully results were obtained by using
the ratios and channel-combinations in the following
3.5.1 Ratio
Landsat 5 TM 1984-06-03
Ratio 5:3 gave a good delineation of mixed spruce
seedling stands (01) from the surroundingdeciduous
forests (figure 3). In addition this ratio gave a good
delineation between lowland vegetation and alpine vege
tation (T0mmervik 1985b). This was also the result by
using the ratio of 5:4:3:2.
Ratio 4:3 gave a good delineation of pine forests
(A4a) from other forests.
SPOT-simulation 1982-06-30
Ratio 3:2 (NIR-quotient) gave a good delineation of
clearcuts, roads, in addition of detection of the var
iation in the farmland. Spruce seedling stands (homo
geneous) were good delineated from the surrounding
3.5.2 Channel combinations
Landsat 5 TM 1984-06-03
The channel combinations of CH 456, CH 532 and CH
754 gave a good delineation of the mixed spruce seed
ling stands from the surrounding deciduous forests,
but not so good as the ratio 5:3. This combination gave
a good delineation of G7 Birch forests of meadow type
and E5a/E5c Grey alder forests (rich type) from poorer
forest cover types. Pine forests were also well detec
Landsat 5 TM 1984-10-02
The channel combinations of CH 432, CH456 and CH 543
gave a good result in detection and delineation of C5
Swamp forests at the river banks and E5a/E5c Grey alder
forests at the hillsides,due to the litter. The chann
el combination of CH 432 was a good basis for inter
pretation of H/ Rich shrub and snowbeds (K2, K6 and
K6'). The channel combination of CH 456 gave a good
délinéation of pine forests (A4a), Spruce seedling
stands (01), Snowbeds (K2 and K6) and Farmland (AA1/
AA2) .
SPOT-simulation 1982-06-30
Channelcombination of CH 321 gave almost same result
of interpretation and detection of vegetation cover
types as Landsat 5 TM, but as a result of the improved
spatial resolution, texture and variation within the
vegetation cover types can be seen. Channel combinat
ion of CH 432 gave a good ability to study texture and
pattern within the vegetation cover types. This is due
to the association between the panchromatic channel
(4) and the multispectral channels 3 and 2. As a res-
Figure 3. Ratio of channel 5 and 3 (5:3) shows a good
delineation of mixed spruce seedling stands from the
surrounding deciduous forests. The area is marked with
an arrow.
ult of improved spatial resolution (10 m), clearcuts
with shrubs were detectable.
3.5.3 General discussion
Hame (1984) has stated that the best results in deline
ation of mixed spruce seedling stands and other very
hardly detectable vegetation cover types, were obtained
using parallelepiped classifications with the first two
principal components calculated of Landsat MSS-imagery.
Jaakkola (1985) has done a similar study on SPOT-sim
ulated imagery and stated that the best results of the
classifications of the imagery, were obtained by using
multi-point (contextual) classification techniques. I
have shown by simple ratio-methods and channel combin
ations elaborated on TM-imagery, that we can delineate
very heterogeneous forest cover- and vegetation cover
types easily.
Second generation satellites such as Landsat 5 TM and
SPOT HRV, providing high spatial resolution between
10 m and 30 m and in the case of Landsat 5 TM, new
spectral channels, will increase the level and accuracy
of digital classification. This study has shown that it
was possible to classify the vegetation cover types
which were furthest phenological develloped, with an
overall accuracy of 90 percent or more, in spite of the
unfortunately fact that the scenes were taken too early
in the springtime or taken to late in the autumn. In
addtition to this the digital classification even went
good in an area with a fairly wide variation in eco
logical niches and a heterogeneous vegetation.
Visual interpretation and classification based on
ratio-techniques and channel combinations was a very
good tool to improve the interpretation of imagery. The
best results in this case were obtained by the TM-sen-
sor of the Landsat 5 satellite.
The middle infrared (MIR) channels of TM were useful
for vegetation classification, especially for deline
ation of heterogeneous forest cover types.
Comparison between Landsat 5 TM-sensor and the simu
lated SPOT HRV-sensor has shown that the two sensor-
systems have almost the same ability to detect and map
vegetation cover types within the area, due to the
higher radiometric resolution for the TM-sensor compa
red to the simulated HRV-sensor. Classification of the
SPOT-simulated imagery showed that vegetation units