Modelling spatial and temporal changes with GIS and Spatial and Dynamic Bayesian Networks. (August 2016)

• Record Type: Journal Article
• Authors: Chee, Yung En; Wilkinson, Lauchlin; Nicholson, Ann E.; Quintana-Ascencio, Pedro F.; Fauth, John E.; Hall, Dianne; Ponzio, Kimberli J.; Rumpff, Libby
• Is Part Of: Environmental modelling & software. Volume 82(2016:Aug.)
• Page Start: 108
• Page End: 120
• Publication Date: 2016-08
• Subjects: Probabilistic graphical models -- State-and-transition models -- Object-oriented -- Adaptive management -- Willow; 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.2016.04.012
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: State-and-transition models (STMs) have been successfully combined with Dynamic Bayesian Networks (DBNs) to model temporal changes in managed ecosystems. Such models are useful for exploring when and how to intervene to achieve the desired management outcomes. However, knowing where to intervene is often equally critical. We describe an approach to extend state-and-transition dynamic Bayesian networks (ST-DBNs) — incorporating spatial context via GIS data and explicitly modelling spatial processes using spatial Bayesian networks (SBNs). Our approach uses object-oriented (OO) concepts and exploits the fact that ecological systems are hierarchically structured. This allows key phenomena and ecological processes to be represented by hierarchies of components that include similar, repetitive structures. We demonstrate the generality and power of our approach using two models — one developed for adaptive management of eucalypt woodland restoration in south-eastern Australia, and another developed to manage the encroachment of invasive willows into marsh ecosystems in east-central Florida. Highlights: We extend Dynamic Bayesian Network technology to model spatial processes. A novel application of object-oriented concepts helps handle BN modelling complexity. We demonstrate proof-of-concept with two environmental management case studies. We include model templates and algorithms for spatio-temporal scenario simulations.