456 
Table 2. Key to the land-use units 
facilitated by the extensive network and 
access roads in the Nairobi area. 
Symbol 
Land-use units 
CT 
coffee, tea 
or pineapple 
F 
forest 
G 
grassland 
B 
buildup 
S 
sisal 
Table 
3. Phototones of the land-use 
units. 
Phototone 
Land-use 
Land-use zone 
black : 
water bodies 
S3,S4,P4 
green : 
bare red soil or quarries 
E.S2.P1,P3 
red : 
bright red : 
soft wood plantations 
M1 ,E 
coffee 
S2,S3,S4 
pineapple 
S3 
foodcrops 
E,S1,S2,S5 
riverbottom vegetation and 
wetlands 
S3,P3 
dark red : 
indigenous forest 
M1,M2,S3,S5 
low density residential 
S5 
pinkish red : 
tea 
S1 f S4 
yellow: 
yellow : 
grasslands 
M2,E,P1,P2,P3 
pinkish yel : 
dairy grasslands 
S4 
brown : 
brown : 
sisal 
P4 
orange brown: 
sisal overgrown with grass 
P3 
blue : 
bright light: 
roads 
P1 ,P2 
blue : industrial 
high density residential 
grasslands with bare grey 
soil 
: commerce and administration 
middle density residential 
E,P1 
S5,P2 
S5,P2 
agricultural activity, building construction 
costs), topography (altitude, drainage, set 
tlement, infrastructure) and the vegetation 
(a function of geomorphology, climate and 
soil) (Morgan 1969). 
For effective planning these elements must 
be considered and assessed and the LANDSAT 
imagery creates the appropriate basis for 
asking pertinent questions. 
Specific conclusions are: 
If properly used LANDSAT data could replace 
the intermediate scale inventory presently 
compiled from aerial photography at scales 
in the 1:100.000 to 1:250.000 range. This 
might be both cost and time effective. In 
particular the amount of information and 
its geographical location presented in the 
LANDSAT scene is an integration of the many 
influences at the earth's surface, and can 
guide the landscape planner's work 
effectively. 
The level of detail available provides an 
appropriate insight into land-use processes 
for the landscape planner at a regional 
level. This is particularly important for 
integrated planning and should contribute 
to better designs and plans for future 
development. 
REFERENCES 
Anderson, J.R. et al. 1976. A Land-use and 
Land-cover Classification System for Use 
with Remote Sensor Data. Geological Survey 
Professional Paper 964. Washington DC: 
US Government Printing Office. 
Duchhart, I. 1986. Inleiding landschapsplan- 
ning in ontwikkelingslanden. Wageningen: 
Landbouw Hogeschool. * 
Government of Kenya 1983. Development Plan 
1984-1988. Nairobi: Government Printer. 
Grootenhuis, F., H.Weeda & K.Kalambo 1986. 
An integrated study of the Nairobi area. 
Rotterdam: Balkema. 
Heetman, H. & I.Duchhart 1979. A short study 
of the landscape planning aspects of the 
Bura irrigation and settlement project, 
Kenya. Wageningen: University of Agriculture 
McHarg, I.L. 1969. Design with Nature. 
Philadelphia: Natural History Press. 
Morgan, W.T.W. 1969 . Nairobi: City and region 
London: Oxford University Press. 
Timberlake, L. 1985. Africa in crisis. London 
and Washington DC: International Institute 
for Environmental Development. 
Tjia, J.G.J. & Y.H.Tjiook 1985. A landscape 
plan for Cilanang Watershed. Wageningen: 
Agricultural University. 
Tolba, M. 1982. Opening address to^jthe Session 
of a Special Character at the 10 r Governing 
Council of the United Natons Environmental 
Program. Nairobi: UNEP. 
LANDSAT data in a photographic print at 
1:100.000 scale proved to be interpretable 
at a surprisingly detailed level. Expanding 
the 1:1M scale land-cover map to a map of 
land-use at 1:100.000 scale required little 
fieldwork. The combination of fieldwork and 
pertinent information from other sources 
led to a rapid understanding of the spa 
tial patterns of interaction between man 
and the bio-physical environment in the 
Nairobi area. 
Interpretation of the colour, tone and tex 
ture of the LANDSAT FCC was relatively easy 
at the 1:100.000 scale. Many components 
could be interpreted and when related to 
existing map information, provided a valua 
ble level of information for mapping land- 
use zones. Checking of interpretation was