SOURCE:

Faculty: Environmental Sciences
Department: Surveying & Geo-informatics

CONTRIBUTORS:

Akinroye, Richard O.
Igbokwe, J. I.

ABSTRACT:

Sequel to the over proliferation of industries in Kaduna coupled with the ethnic-religious crisis in many other parts of the northern Nigeria, resulting to the migration of very many people to Kaduna, the natural resources in the area, especially land and space became over consumed, causing many other environmental problems over the years. This study, therefore, was conducted to model and map landcover and landuse changes in Kaduna using Cellular Automata and multiple logistic regression with a view to ascertaining changes in the area. The objectives of this research work includes; classifying the multi-temporal satellite imageries of landsat ETM+ into built-up area, open space, farmland, vegetation and water bodies using 2004, 2009 and 2014 imageries; also to model the landcover and landuse changes of Kaduna and Environs using Cellular Automata and to predict the pattern of changes (25 years) with multiple logistic regression. The landsat ETM + of years 2004, 2009 and 2014 were used. The landsat imageries for the different epochs were classified and accuracy assessment carried out. Multiple logistic regression was used to predict changes. Built-up area increased from 16% in the year 2004 to 19% in the year 2009 and 21% in the year 2014. Open space decreased from 8% in the year 2004 to 3% in the year 2009, but increased to 5% in the year 2014. Water bodies increased from 1% in the year 2004 to 2% in the year 2009 and remained the same in year 2014. Cellular Automata are explicitly spatial in which dynamics are applied on data. Multiple logistic regression utilize the relationship between two or more quantitative variables to enable prediction to be carried out. The result of the hypothesis testing revealed that vegetation and farmland transited to Built-up area. The study recommended that landuse mapping should be carried out at regular intervals for effective and efficient planning of our cities.