The role of GIS in fuelwood extraction management in rural southern Africa savanna woodlands

Southern African savannas are under increasing threat of over-exploitation due to higher demands for fuelwood and charcoal by low income communities. In some electrified communities, the high cost of electricity has generally prevented a move away from dependency on bio-energy. In Mozambique, Tanzania and Zambia, charcoal from communal woodlands is exported to urban centres for sale to low-income urban dwellers. Intensive harvesting practices and demands for arable farming land are contributing to the continued decrease in the availability of plant species preferred for fuelwood and/or charcoal. The exploitation of traditional biomass systems for cash and/or mercantile purposes (charcoal and lumber) is also leading to accelerated losses of natural forests and biodiversity, as well as creating local scarcity of biomass. However, quantitative data on woody biomass preferred for fuelwood/charcoal and its spatial distribution is often lacking. Where such information exists, it is too site specific and difficult to extrapolate to macro levels for rural domestic energy planning purposes. High costs of both ground-based assessment methods and high resolution satellite images tend to contribute to this scarcity of convertible biomass data. Spaceborne radar remote sensing is offering exciting opportunities for estimating standing woody biomass volumes at landscape scales. However, not all known biomass stocks are accessible to rural communities due to long distances from households, land tenure regimes, geographic location of resource bases and/or transport infrastructure.
Spatial analysis in GIS environment enables the modelling of the natural and anthropogenic factors that influence the accessibility and availability of fuelwood and/or charcoal biomass resources. This paper will present a conceptual framework of a GIS-based management information system that can be used to estimate the disposable woody biomass available to rural dwellers in any given settlement. The provision of such quantitative data will greatly enhance decision-making by energy modellers, woodland dynamics practitioners and socio-economic managers.