Bayesian belief networks as a versatile method for assessing uncertainty in land-change modeling
Land-use and land-cover change modeling helps us to understand the driving factors and impacts of human-induced land changes better, and depict likely future development paths. 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. We demonstrate that
a higher uncertainty in the land-change modeling process does not necessarily lead to a lower accuracy of the modeling outcome. Moreover, we show that the different uncertainty sources only slightly influence the ratio between quantity disagreement and allocation disagreement for the modeling
outcome. We conclude that uncertainty is inherent in land-change modeling, and that future studies should address this uncertainty more explicitly to improve the robustness of modeling outcomes for science and decision-making.
Keywords: error; land-change processes; probability; spatiotemporal modeling; uncertainty assessment
Document Type: Research Article
Affiliations: Geography Department, Humboldt-Universität zu Berlin, 10099, Berlin, Germany
Publication date: 02 January 2015
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