A GIS plug-in for Bayesian belief networks: Towards a transparent software framework to assess and visualise uncertainties in ecosystem service mapping. (September 2015)

• Record Type: Journal Article
• Authors: Landuyt, Dries; Van der Biest, Katrien; Broekx, Steven; Staes, Jan; Meire, Patrick; Goethals, Peter L.M.
• Is Part Of: Environmental modelling & software. Volume 71(2015:Sep.)
• Page Start: 30
• Page End: 38
• Publication Date: 2015-09
• Subjects: BBN -- ES -- Spatial modelling -- Decision support -- Uncertainty maps -- Uncertainty analysis; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2015.05.002
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: The complexity and spatial heterogeneity of ecosystem processes driving ecosystem service delivery require spatially explicit models that take into account the different parameters affecting those processes. Current attempts to model ecosystem service delivery on a broad, regional scale often depend on indicator-based approaches that are generally not able to fully capture the complexity of ecosystem processes. Moreover, they do not allow quantification of uncertainty on their predictions. In this paper, we discuss a QGIS plug-in which promotes the use of Bayesian belief networks for regional modelling and mapping of ecosystem service delivery and associated uncertainties. Different types of specific Bayesian belief network output maps, delivered by the plug-in, are discussed and their decision support capacities are evaluated. This plug-in, used in combination with firmly developed Bayesian belief networks, has the potential to add value to current spatial ecosystem service accounting methods. The plug-in can also be used in other research domains dealing with spatial data and uncertainty. Highlights: Spatial heterogeneity of ES delivery requires spatially explicit accounting methods. Limited availability of primary data promotes the use of knowledge-based BBN models. The proposed GIS BBN plug-in offers a standardized approach to model ES delivery. Diverse probabilistic output maps can be produced to support decision making. The preferred type of output map depends mainly on end-user requirements.