510 
is an added advantage. 
2 OBJECTIVES 
The main objective now is to develop further and 
use the method for accurate and precise inventory 
ing, mapping and monitoring of land use and land 
cover for regional as well as national resource 
management. Specifically, therefore, the method is 
being used, among other things, to estimate: 
1. crops (and other class types) hectarages; 
2. crops (and other class types) distribution and 
intensity zones; 
3. crop mix zones; and 
4. spread of cultivation into the rangeland. 
Outlined below is a summary of the procedures and 
preliminary results for a land use and land cover 
survey for Meru District in 1985. Similar surveys, 
whose results are briefly compared, have been 
accomplished for 9 other districts and more are in 
the process. 
3 METHODS 
The method entailed multistage sampling techniques 
using low altitude aerial vertical photographs. 
The district covering approximately 9850 km^ was 
divided into 5x5km square sampling units using the 
UTM grid system and east-west sampling flight lines 
were placed 5km apart. Two sample photographs were 
taken systematically at every 2.5km UTM grid inter 
sections in the high potential region and at 5km in 
the marginal rangelands. Flight height was 
approximately 488m above ground level. Navigation 
used the Global Navigation System (GNS) fitted in 
the twin-engine Partenavia aircraft. 
The photographic system used was a 35mm Nikon F3 
camera with a 20mm lens and 35mm ektachrome 200 
slide films. This gave positive transparencies 
covering approximately 560m x 850m on the ground, 
with a scale of 1:24,380. The positive trans 
parencies were each overlayed and fixed with 91 
(7x13) systematically placed dots on a transparency 
and observed under a hand held slide viewer or 
sometimes projected onto a screen. Crop and other 
cover classes coincident with each dot were identi 
fied by photointerpretation. The area covered by 
each class type was then estimated by the pro 
portion of dots for that class type to the total 
number interpreted in the 5x5km sample unit and 
expressed as a percentage. This was the basis for 
the construction of overlays for each class type in 
terms of distribution, density, and crop mix zones. 
A framework of procedures for regional and field 
familiarization before and during interpretation 
were established and followed to increase accuracy 
and consistency in interpretation. Before actual 
district-wide photointerpretation began preparatory 
procedures involved literature review; increasing 
relevant local knowledge through discussions; 
choosing one or two training photosamples and 
making trial runs on them in interpretation and 
then ground checking them for every major ecologi 
cal region in the district; and this last exercise 
enhanced the interpreters' familiarization of the 
land use and land cover classification system which 
was being used. Procedures were also laid down on 
how to classify dots falling on non-pure class 
types like mixed fields, field boundaries and 
single trees to avoid personal bias. 
The identification of land use and land cover 
types for every dot on the photograph was done for 
at least the first three levels of classification 
coarseness as described by Anderson et al (1976) 
and modified for local adoption. Accuracy and 
consistency of identification of class types were 
assessed using 'confusion tables' as outlined by 
Kalensky (1975,1978) and linear regression analysis 
between photointerpretation and field sample checks 
at every level of identification using aggregate 
data from every photo-sample field checked. The 
correlation coefficient was considered a measure of 
consistency in interpretation. Near perfect inter 
pretation would have a regression line of Y = X and 
a correlation coefficient (r) > 0.90. 
Mapping was partly done using Map Analysis 
Package (MAP) a Geographical Information System 
(GIS) software developed at Harvard and Yale 
Universities. 
4 RESULTS AND DISCUSSION 
A total of 443 photographs were analyzed for 263 
sample units. Three areas of the district, Mount 
Kenya National Park and Forest; Meru National Park 
area; and the Northern Conservation Area, were not 
surveyed mainly because their uses and cover types 
are well documented and partly because of terrain 
and air turbulence. 
For accuracy and consistency tests twenty-seven 
(27) photo samples were interpreted and ground 
checked by each of the three photointerpreters. 
The dots overlay method was used in assessing their 
interpretation accuracy and consistency. Each dot 
was fixed and examined by each interpreter and 
therefore making it possible to compare their 
performance. A total of 2457 dots were checked in 
the 27 photos. Correlations were carried out 
between photointerpretation and ground truth for 
those class types with sufficient data and occur 
rence for the three interpreters separately and for 
the three combined to give an average. Interpreta 
tion for the major cover types was fairly similar 
between the three interpreters. 
The interpretation accuracy test showed that land 
under cultivation as a class was interpreted with 
an average accuracy of 91% with r=0.93; improved 
and unimproved pasture 77% with r=0.89; bushlands 
71% with r=0.90;; forests 84%; and the rest of the 
third level classes had few (<50) dots and photo 
sample occurrence for analysis. 
For specific crops and cover classes, maize was 
interpreted with an average accuracy of 83% with 
r=0.94; coffee 86%; tea 85%; bananas 62%; cotton 
49%; ploughed land 72%. The rest had insufficient 
data for analysis. 
Table 1 below gives the percent cover for the 
major class types condensed from over 35 specific 
class types identified in the district. For 
example, of the cultivated land, 38% was under 
maize, 14% was under coffee, 12% was ploughed, 9% 
was fallow and the remainder 27% was distributed 
among 16 different crops. Distribution and 
intensities for each of the extensive class type 
was mapped and contouring approximated using MAP. 
The systematic dot method was therefore found to be 
quick and reliable for estimating areas of each 
class type for the whole district; a difficult dot 
could be discussed among the interpreters to 
reach a consensus. Thus it is contended that 
district-wide interpretation accuracy and con 
sistency should be higher than that given above by 
the accuracy tests for most class types. 
Previous analyses for the other districts 
invariably involved the projection of the slide 
photos onto a screen which had fifty random dots 
on which class types were identified. For each 
district an attempt was made to sample survey the 
whole district surface area but large tracts of 
water bodies and extensive gazetted forest 
reserves were often omitted to reduce costs and 
expedite the process to cover much more critical 
areas. Table 1 is also a summary of the percent 
area under the major land use and land cover types 
found in the sampled area for the nine districts. 
(The indicated areas in brackets are the sampled 
areas only, which may not necessarily be the area 
of the district).