In this paper, we use decision trees to construct models for predicting vegetation types from environmental attributes in a salt marsh. We examine a method for evaluating the worth of a decision tree and look at seven sources of uncertainty in the models produced, namely algorithmic, predictive, model, scenario, objective, context and scale. The accuracy of prediction of types was strongly affected by the scenario and scale, with the most dynamically variable attributes associated with poor prediction, while more static attributes performed better. However, examination of the misclassified samples showed that prediction of processes was much better, with local vegetation type-induced patterns nested within a broader environmental framework.