511
Table l. Land use and land cover statistics for 10 districts in Kenya, as a percent of the total land
surveyed.
Land use and land cover
District
Class type
Meru
(6575)
Kisii
*(2375)
South
Kisumu Nyanza
(2200) (5500)
Bung-
oma
(2475)
Mur-
anga
(1875)
Nyand-
arua
(2400)
Busia
(1450)
Ker-
icho
(4800)
Trans-
nzoia
(2075)
Cultivated (crops, ploughed)
25.1
43.9
27.9
15.1
41.3
52.6
17.1
30.3
20.0
39.9
Pasture (improved, unimproved)
20.1
32.5
36.2
71.2
36.0
20.1
58.0
51.2
45.5
45.2
Bush and Shrublands
38.3
0.0
20.6
5.6
9.8
6.5
11.8
6.3
12.4
3.1
Forest/Woodlands
8.1
8.2
0.0
0.4
9.0
0.0
0.0
0.0
15.4
4.5
Transportation (roads, paths, etc.)
1.3
3.1
3.1
1.5
2.9
4.7
2.1
1.9
2.2
1.4
Structures (buildings)
0.6
2.4
1.2
0.5
0.0
1.8
0.0
0.7
0.1
0.0
Woodlots
1.0
0.0
0.0
0.2
0.0
9.6
0.9
0.0
0.0
4.4
Hedges
0.6
8.4
6.6
2.2
0.7
4.7
1.8
4.2
3.5
0.6
Swamps/Rivers
0.8
0.6
4.2
0.7
0.2
0.0
5.1
2.8
0.7
0.1
Barren (rocks, gulleys)
2.3
0.0
0.4
0.3
0.0
0.0
0.9
0.0
0.0
0.8
Others
1.9
0.9
0.0
2.3
0.0
0.0
2.3
2.6
0.1
0.0
Total
100.1
100.0
100.2
100.0
99.9
100.0
100.0
100.0
99.9
100.0
Sources: Agatsiva 1984, 1985; Epp et al 1983; Muchoki 1985a,b; Mwendwa 1984, 1985; Ottichilo 1985; Peden et
al 1984.
* area surveyed in km^ - See text.
Significant changes in future of any of these
statistics in any district in area or amounts will
be a reflection of a process of changes going on as
a reaction to certain pressures or certain manage
ment policies. A more detailed comparison between
districts and detection of changes will be more
useful when the data is disaggregated into differ
ent ecological strata.
The use of aerial sample photography for the
mapping of resources has been advocated and dis
cussed before by many authors for example Berry and
Baker (1968) and Robertson and Stoner (1970).
However, its application to a large scale mapping
project has been lacking. The Kenyan project was
adopted after a trial run in Kisii District (Epp,
Killmayer and Peden 1983). In the Kisii study and
those that followed the photo samples were strati
fied and analyzed on the basis of sub-district
administration units. Thus when the average per
cent land covered by a class type was mapped, the
distribution was depicted as evenly spread through
out the administration unit through differential
shading or pie charts. However for Meru District,
class types' intensity and extents were first
mapped independent of any factor. This method of
mapping class type and intensities gives a picture
close to real distribution within any type of
stratification consequently overlayed. Stratifica
tion can be on basis of administration units,
soils, vegetation, climate or human population for
various types of studies (e.g. factors affecting a
class type distribution) in an effort to make
rational development plans. Present generations of
GIS systems with analytical capabilities can now
perform these functions much faster.
A major use of this type of mapping is the detec
tion and measure of the movement and cultivation
activities into the marginal areas. Epp and
Killmayer (1982) suggested that areas with land 10%
or more under cultivation be designated as 'agri
cultural'. Jaetzold and Schmidt (1982) have how
ever defined in details agroecological zones in
Kenya in which agricultural areas are ecologically
delineated. Using these two criteria, newly set
tled areas as well as cultivation extending beyond
ecologically acceptable boundaries can be detected
using the photosample methodology described here
and by map overlays.
A recent experimentation using similar principles
of methodology has been reported from Nigeria with
success in providing fairly accurate statistics for
planning (Bauchi 1984; Nigeria 1984). Mapping was
done using pie charts and proportionate circles on
each grid cell, to depict coverage or intensity of
a class type in that cell.
5 CONCLUSION
The method described above has proved useful in
crop hectarage estimation and distribution studies
in the intensive agricultural areas of Kenya where
a great variety of crops are grown in small land
segments. Accuracy and consistency were found high
in the second and third classification levels and
in those finer class types which have extensive
areal coverage like maize. However, some
vegetation cover type classifications need more
refined description and definition to reduce their
class confusions, for example between unimproved
pasture and bushlands, which was responsible for
their relatively lower interpretation accuracy.
It is anticipated that the infornmtion from all
the districts will eventually be combined to give a
national overview for every class or combination of
class types. This is expected to be possible
because the data is being collected using same
method, measurement units (percent cover) as well
as the UTM system base maps for every district, and
thus produce a detailed land use and land cover map
of Kenya. It is also anticipated that it will be
possible to detect areas of land use conflicts and
potential desertification frontiers.
6 ACKNOWLEDGEMENT
This study was funded by the Kenya Government
through KREMU, while our studies, including the
project time, were made possible jointly by the
Kenya Government and the Canadian International
Development Agency (CIDA). We are grateful to
both. Professor Tom Henley of the Natural
Resources Institute, University of Manitoba, and
