Abstract:

Like other countries in the Sub-Saharan Africa, Uganda is not an exception to the effects of land
use/cover changes on the environment. This study aimed at analyzing land use/cover trends in
Kirima Sub/County-Kanungu District. Specifically the study intended to; establish the main
drivers of land use/cover change and determine their magnitude and trend for the last 35 years. In
establishing the main drivers of land use/cover change the study utilized household
questionnaires and a focus group discussion guide. A total of 65 respondents included in the
study were randomly selected from the local council members list for questionnaire
administration while 12 participants were included in the focus group discussions from eight
randomly sampled villages in the sub-county. The data was analyzed using a Logistic Regression
in SPSS Windows (10.0). A series of satellite imagery (1975, 1987 and 1999) were utilized to
determine the magnitude of land use/cover change using unsupervised classification in Integrated
Land and Water Information Systems (ILWIS 3.3) Academic software. Findings indicate that
household size and the weak environmental laws were the main underlying drivers of land
use/cover changes. Other drivers included; type of crops grown, extension agents’ visits, and
customary land tenure, all were statistically significant predictors of land use/cover change
(P<0.05). The magnitude of small scale farming (non-uniform) largely increased by 5% from
1975 to 1999 while areas covered by Tropical high forest relatively decreased by 16% between
1975 and 1987 but slightly increased by 1% in 1999. The areas covered by wetlands
comparatively increased by 4% from 1975 to 1987 and by 1999 they slightly decreased by 3%
while the woodland areas also moderately decreased by 3% from 1975 to 1987 and to some
extent increased by 2% in 1999 in Kirima sub-county. The time series regressions showed that
small scale (non-uniform) farming (0.829) and Tropical High Forest (0.697) had relatively strong
regression strength and good fit compared to wetlands (0.053) and woodlands (0.049) with very
weak regression strength and a weak fit. This study has shown that it is possible to use GIS and
Remote Sensing to quantify change patterns at micro scale to provide territorially differentiated
statistics.