Bayesian belief networks as a versatile method for assessing uncertainty in land-change modeling

Uncertainty associated with various steps in the modeling process substantially influences the reliability of the results, but until now it has only rarely been addressed. In this study, we explore uncertainty in land-change modeling using a probabilistic approach based on Bayesian belief networks.

We apply this approach to a case study of deforestation in the Brazilian Amazon and identify three modeling steps as sources of uncertainty: model structure, variable selection, and data preprocessing. For these three steps, we quantify the uncertainty and the respective impact on the outcome accuracy. The results indicate remarkable uncertainties in each of the steps.