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

446
The 1:1M road map of Kenya provided the geo
graphical orientation. The multispectral
additive viewer was used to study various
conbinations of bands 4,5,6 and 7 separately.
Band 5 is used for the black and white repro
duction in this paper, showing maximum detail
2.2 Method
Yellow and Green: (E) Even less vegetation
and very dry condition, resulting in in
crease of reflection.
Yellow, Blue and
^rown: (P) Vegetation cover is
further diminished and the grey soil gives
an even higher reflection than in zones E
or S.
Visual interpretation of the LANDSAT image
identified four photo-tonal zones, resulting
in the physiographic map (Figure 2).
Two traverses were chosen which crossed all
of the zoned types, and fieldwork was carried
out along these cross-sections (Figure 1, 3)
to identify the existing land-cover. A data
sheet was designed to record field informa
tion at intervals of approximately 5 km.
The variables recorded included altitude,
landform, drainage, vegetation and land-use.
The land-cover/land-use classification system
of the United States Geological Survey was
applied (Anderson 1976).
Because of the construction of the FCC, these
zones are mostly identified by vegetation..
However, there are more sub-divisions of vege
tation possible, and the physiography is more
properly identified as follows:
Mountains (M) uniformly red
Slopes (S) mottled red and green
Escarpment (E) yellowish
Plains
(P)
yellow-blue
Figure 2. Physiographic map.
SK-N
E
Compilation of the physiographic data veri
fied the physiographic map initially compiled
from photo-tones and colours. The additional
field data led to the production of a land-
cover map at 1:1M (Figure 4), the same scale
as the initial LANDSAT image. This landcover
map is more detailed than the physiographic
map, and represents the limit of interpreta
tion at this scale.
3 RESULTS
3.1 Physiographic map 1:1M (Figure 2)
From an initial inspection of the LANDSAT
data in FCC transparency at 1:1M scale, the
boundaries shown in Figure 2 were drawn.
These boundaries were drawn around areas
which had similarity in colour, texture and
tone, each representing a physiographic zone,
identified as mountain (M), slope (S),
escarpment (E),and plains (P). The colour
analysis shows the classes to be roughly
identified as follows (Table 1):
Dark Red: (M) Heavy vegetation or
forest
Red and Green: (S) Vegetation is less
dense and probably includes bare soil.
The January image presents the end of the
dry season, when certain crops have already
been harvested.
Figure 3. Physiographic cross-sections.
Table 1. Physiographic elements.
Mapping unit
Phctntone
Altitude
Drainage
(M) Mpuntain
dark red
2100 m
parallel
(S) Slppes
red and green
1500 m
1800-2100
m
parallel
parallel
(E) Escarpment
yellow and green
1800-2100
m
internal
(P) Plain
yellow, blue and
brown
1500 m
1800 m
dendritic
dendritic
These boundaries were also
presence in the individual
examined
black and
for their
white
transparencies of the LANDSAT data. Figure 2
shows the relationship between the physio
graphic boundaries and the band 5 monochrome
data. The physiographic regions can be iden
tified by tone and texture on the monochrome
data of the other bands. Field checking of
these boundaries and reference to available
topographic maps showed that the four physio
graphic regions had the following characte
ristics (Table 1):