Bayesian Networks in Environmental Modelling

Environmental Modelling & SoftwareEnvironmental Modelling & Software, Available online 02 July 2011

P.A. Aguilera, A. Fernández, R. Fernández, R. Rumí, and A. Salmerón


  • We review papers in which Bayesian networks have been applied in environmental modelling.
  • The potential of Bayesian networks is still unexploited in the environmental science field.
  • Some recommendations for future works on this topic are proposed.

“Bayesian networks (BNs), also known as Bayesian belief networks or Bayes nets, are a kind of probabilistic graphical model that has become very popular to practitioners mainly due to the powerful probability theory involved, which makes them able to deal with a wide range of problems. The goal of this review is to show how BNs are being used in environmental modelling. We are interested in the application of BNs, from January 1990 to December 2010, in the areas of the ISI Web of Knowledge related to Environmental Sciences. It is noted that only the 4.2% of the papers have been published under this item. The different steps that configure modelling via BNs have been revised: aim of the model, data pre-processing, model learning, validation and software. Our literature review indicates that BNs have barely been used for Environmental Science and their potential is, as yet, largely unexploited.”

Integrating Remote Sensing Techniques, Geographical Information Systems (GIS), and Stochastic Models for Monitoring Land Use and Land Cover (LULC) Changes in the Northern Coastal Region of Nayarit, Mexico

GIScience & Remote SensingGIScience & Remote Sensing, Volume 48, Number 2 / April-June 2011

César Alejandro Berlanga-Robles and Arturo Ruiz-Luna

“Land use and land cover (LULC) changes in northern Nayarit, Mexico were estimated using post-classification change detection methods and a Markov chain model. Three thematic maps were generated by classifying Landsat images from 1973, 1900, and 2000, which were then overlaid to generate three change-detection matrices to assess the intensity and direction of changes. Between 25% and 30% of the region displayed LULC changes, attributable to a stochastic behavior that can be modeled with a first-order Markov chain. The steady-state distribution estimates indicate that the LULC patterns in the region have not yet reached equilibrium and predict the expansion of the agricultural boundaries.”

Building Population Mapping with Aerial Imagery and GIS Data

International Journal of Applied Earth Observation and GeoinformationInternational Journal of Applied Earth Observation and Geoinformation, Volume 13, Issue 6, December 2011, Pages 841-852

Serkan Ural, Ejaz Hussain, and Jie Shan


  • The proposed model considers the effects of different types of residential buildings.
    Building footprints and heights are derived from images, digital terrain and surface models.
    GIS data separate houses from apartments.
    Estimated building populations are evaluated with reference to the known census records.

“Geospatial distribution of population at a scale of individual buildings is needed for analysis of people’s interaction with their local socio-economic and physical environments. High resolution aerial images are capable of capturing urban complexities and considered as a potential source for mapping urban features at this fine scale. This paper studies population mapping for individual buildings by using aerial imagery and other geographic data. Building footprints and heights are first determined from aerial images, digital terrain and surface models. City zoning maps allow the classification of the buildings as residential and non-residential. The use of additional ancillary geographic data further filters residential utility buildings out of the residential area and identifies houses and apartments. In the final step, census block population, which is publicly available from the U.S. Census, is disaggregated and mapped to individual residential buildings. This paper proposes a modified building population mapping model that takes into account the effects of different types of residential buildings. Detailed steps are described that lead to the identification of residential buildings from imagery and other GIS data layers. Estimated building populations are evaluated per census block with reference to the known census records. This paper presents and evaluates the results of building population mapping in areas of West Lafayette, Lafayette, and Wea Township, all in the state of Indiana, USA.”