Modelling spatial and temporal changes with GIS and Spatial and Dynamic Bayesian Networks


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.

Año de publicación:

2016

Keywords:

  • adaptive management
  • State-and-transition models
  • object-oriented
  • probabilistic graphical models
  • Willow

Fuente:

scopusscopus

Tipo de documento:

Article

Estado:

Acceso restringido

Áreas de conocimiento:

  • Simulación por computadora

Áreas temáticas de Dewey:

  • Sistemas
Procesado con IAProcesado con IA

Objetivos de Desarrollo Sostenible:

  • ODS 15: Vida de ecosistemas terrestres
  • ODS 13: Acción por el clima
  • ODS 17: Alianzas para lograr los objetivos
Procesado con IAProcesado con IA