Use of conditional probability networks for environmental monitoring

Causal or conditional probability networks (CPNs) are shown to provide a natural framework for combining a time sequence of classified satellite images with other maps for environmental monitoring. The key features of CPNs are described by way of application to an example involving the monitoring of salinization of farmland over time using satellite images and an ancillary dataset derived from a digital terrain model. It is shown that CPNs can be used to improve mapping accuracies by incorporating knowledge about the spatial and temporal variation of the map classes of interest. The methods provide a practical solution to the challenging problem of mapping and monitoring salt in farmland. The representation and propagation of uncertainty within this framework is discussed, as well as the spatial and temporal prediction of images and maps.