For over twenty years, Obuasi Municipality, Ghana, has experienced land-cover change arising from gold mining and urbanisation. This project quantified the land-cover changes that have taken place and projected likely...For over twenty years, Obuasi Municipality, Ghana, has experienced land-cover change arising from gold mining and urbanisation. This project quantified the land-cover changes that have taken place and projected likely future land-cover. An integration of EO （earth observation）, GIS （geographical information science） and Stochastic Modelling was examined. Post-classification Change Detection employed Landsat TM or ETM＋ images in 1986, 2002 and 2008. Subsequently, Markov Chain Analysis projected the land-cover distribution for 2020. Seven broad land-use and land-cover classes were identified and mapped, namely： built-up areas, mine sites tailing ponds barren land forestland farmland and rangeland. The results obtained for the 2008 to 2020 projection revealed a continuous expansion of built-up areas （1.63%）, mine sites （0.89%） and farmland （3.4%）, and a reduction of forestland （4.17%） and rangeland （2.59%）. Despite the advent of very high resolution satellite imagery, this use of EO and GIS technology focussed on low-cost and lower resolution satellite imagery, coupled with Markov Modelling and was found to be beneficial in describing and analysing land-cover change processes in the study area, and was hence potentially useful for strategic planning purposes.展开更多
文摘For over twenty years, Obuasi Municipality, Ghana, has experienced land-cover change arising from gold mining and urbanisation. This project quantified the land-cover changes that have taken place and projected likely future land-cover. An integration of EO （earth observation）, GIS （geographical information science） and Stochastic Modelling was examined. Post-classification Change Detection employed Landsat TM or ETM＋ images in 1986, 2002 and 2008. Subsequently, Markov Chain Analysis projected the land-cover distribution for 2020. Seven broad land-use and land-cover classes were identified and mapped, namely： built-up areas, mine sites tailing ponds barren land forestland farmland and rangeland. The results obtained for the 2008 to 2020 projection revealed a continuous expansion of built-up areas （1.63%）, mine sites （0.89%） and farmland （3.4%）, and a reduction of forestland （4.17%） and rangeland （2.59%）. Despite the advent of very high resolution satellite imagery, this use of EO and GIS technology focussed on low-cost and lower resolution satellite imagery, coupled with Markov Modelling and was found to be beneficial in describing and analysing land-cover change processes in the study area, and was hence potentially useful for strategic planning purposes.
