This thesis addresses three research gaps in published literature. These are, the absence of Object
Based Image Analysis (OBIA) methods for urban Land Use and Land Cover (LULC) analysis in
Nigeria; the inability to use Nigeriasat-1 satellite data for urban LULC analysis and monitoring urban
growth in Nigeria with Shannon’s Entropy Index.
Using Abuja as a case study, this research investigated the nature of land use/land cover change
(LULCC). Specific objectives were: design of an object based classification method to extract urban
LULC; validate a method to extract LULC in developing countries from multiple sources of remotely
sensed data; apply the method to extract LULC data; use the outputs to validate an Urban Growth
Model (UGM); optimise an UGM to represent patterns and trends and through this iterative process
identify and prioritise the driving forces of urban change; and finally use the outputs of the land use
maps to determine if planning has controlled land use development.
Landsat 7 ETM (2001), Nigeriasat-1 SLIM (2003) and SPOT 5 HRG (2006) sensor data were merged
with land use cadastre in OBIA, to produce land use maps. Overall classification accuracies were
92%, 89% and 96% respectively. Post classification analysis of LULCC indicated 4.43% annual
urban spread. Shannon’s Entropy index for the study period were 0.804 (2001), 0.898 (2003) and
0.930 (2006). Cellular Automata/Markov analysis was also used to predict urban growth trend of
0.89% per annum.
For the first time OBIA has been used for LULC analysis in Nigeria. This research has established
that Nigeriasat-1 data can contribute to urban studies using innovative OBIA methods. In addition,
that Shannon’s Entropy Index can be used to understand the nature of urban growth in Nigeria.
Finally, the drivers of LULCC in Abuja are similar to those of planned capital cities in other
developing economies.
Land use developments in Abuja can provide an insight into urban dynamics in a developing
country’s capital region. OBIA, Shannon’s Entropy Index and UGM can aid urban administrators and
provide information for sustainable urban planning and development.