A Land Cover Change Synthesis Study for the GLOWA Volta Basin in West Africa using Time Trajectory Satellite Observations and Cellular Automata Models

Quantifying the regional effects of land cover change is imperative to improve future hydrological budget estimates within large river basins. In this study we aim to utilize binary logistic regressions models within a cellular automation (CA) modeling environment to find causalities for satellite remote sensing measured land cover change (LCC). We used 30-meter Landsat and 250-meter MODIS time-series observations to map LCC for different time trajectories in two large study areas in Burkina Faso and Ghana. We used the FAO land cover classification system (LCCS) legend to map LCC processes from the satellite trajectories. Socio-economic data on population density, distances to roads, and biophysical data sets were processed in the CA model. The neighborhood effect of the change predictors were accounted for by using an enrichment factor. The relationship between the satellite derived LCC and the major biophysical and socio-economic drivers showed that population density, and the increase of cropland areas are responsible for the conversion of forests and woodlands. This was observed for both study areas.