This work sought and identified the different types of land covers;detected the changes in land cover and examined the driving forces of such changes in Ibiono Ibom Local Government Area of Akwa Ibom State, Nigeria. S...This work sought and identified the different types of land covers;detected the changes in land cover and examined the driving forces of such changes in Ibiono Ibom Local Government Area of Akwa Ibom State, Nigeria. Satellite images data of the area for 1986 and 2006 were collected for analysis. Household level social survey was conducted to generate data on the socio-economic variables. The images were subjected to principal component analysis to reduce and compress the data while the supervised image classification algorithm was applied to process the images into different land cover classes. The change detection algorithm in Erdas imagines was applied to measure and calculate the land cover change of the area. The result of the social survey revealed that 58% of the occupation was land based while in terms of yearly income, 65 percent earned less than $300 (#48000). The change detection carried out revealed an increase in areas of secondary forest while bush fallow recorded a reduction up to 34.02 hectares (56.55%) within the study period. Socio-economic variables of poor income and mode of land preparation for farming were the major drivers of change. Based on the findings, it is recommended that the slash and burn mode of land preparation be discouraged.展开更多
文摘This work sought and identified the different types of land covers;detected the changes in land cover and examined the driving forces of such changes in Ibiono Ibom Local Government Area of Akwa Ibom State, Nigeria. Satellite images data of the area for 1986 and 2006 were collected for analysis. Household level social survey was conducted to generate data on the socio-economic variables. The images were subjected to principal component analysis to reduce and compress the data while the supervised image classification algorithm was applied to process the images into different land cover classes. The change detection algorithm in Erdas imagines was applied to measure and calculate the land cover change of the area. The result of the social survey revealed that 58% of the occupation was land based while in terms of yearly income, 65 percent earned less than $300 (#48000). The change detection carried out revealed an increase in areas of secondary forest while bush fallow recorded a reduction up to 34.02 hectares (56.55%) within the study period. Socio-economic variables of poor income and mode of land preparation for farming were the major drivers of change. Based on the findings, it is recommended that the slash and burn mode of land preparation be discouraged.
