The objective of this study is to better understand the complexity of deforestation processes in southern Cameroon by testing a multivariate, spatial model of land-cover change trajectories associated with deforestation. The spatial model integrates a spectrum of independent variables that characterise land rent on a spatially explicit basis. The use of a time series of high-spatial resolution remote sensing images (Landsat MSS and SPOT XS), spanning two decades, allows a thorough validation of spatial projections of future deforestation. Remote sensing observations reveal a continuous trend of forest clearing and forest degradation in southern regions of Cameroon, but with a highly fluctuating rate. A significant proportion of the areas subject to a land-cover conversion experienced other changes in the following years. The study also demonstrates that modelling land-cover change trajectories over several observation years allows a better projection of areas with a high probability of change in land-cover than projecting such areas on the basis of observations from the previous time period alone. Statistical results suggest that, in our southern Cameroon study area, roads mostly increased the accessibility of the forest for migrants rather than providing incentives for a transformation of local subsistence agriculture into market-oriented farming systems. The spatial model developed in this study allows simulations of likely impacts of human actions, leading to a transformation of the landscape (e.g., road projects) on key landscape attributes (e.g., biodiversity). Currently, several road projects or major logging concessions exist in southern Cameroon.
Topic: deforestation,land use,change,human activity,roads,remote sensing,landscape model
Publication Year: 2000
Source: Annals of the Association of American Geographers 90(3): 467-494