Abstract
In this paper we show how a Bayesian network of inference can be used with a GIS to combine information from different sources of data for classification. Data may include satellite sensor images, topographic maps, geological maps etc, each one with its own resolution and accuracy. We show how this uncertainty in the input data can be incorporated in the network and present various methods by which the conditional probability matrices used by the network can be constructed. We demonstrate our approach within the framework of the problem of assessing the risk of desertification of some burned forests in the Mediterranean region.
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