Bayesian networks for raster data (BayNeRD): plausible reasoning from observations

Mello, Marcio Pupin; Risso, Joel; Atzberger, Clement; Aplin, Paul; Pebesma, Edzer; Vieira, Carlos Antonio Oliveira; Rudorff, Bernardo Friedrich Theodor

Abstract

This paper describes the basis functioning and implementation of a computer-aided Bayesian Network (BN) method that is able to incorporate experts’ knowledge for the benefit of remote sensing applications and other raster data analyses: Bayesian Network for Raster Data (BayNeRD). Using a case study of soybean mapping in Mato Grosso State, Brazil, BayNeRD was tested to evaluate its capability to support the understanding of a complex phenomenon through plausible reasoning based on data observation. Observations made upon Crop Enhanced Index (CEI) values for the current and previous crop years, soil type, terrain slope, and distance to the nearest road and water body were used to calculate the probability of soybean presence for the entire Mato Grosso State, showing strong adherence to the official data. CEI values were the most influencial variables in the calculated probability of soybean presence, stating the potential of remote sensing as a source of data. Moreover, the overall accuracy of over 91% confirmed the high accuracy of the thematic map derived from the calculated probability values. BayNeRD allows the expert to model the relationship among several observed variables, outputs variable importance information, handles incomplete and disparate forms of data, and offers a basis for plausible reasoning from observations. The BayNeRD algorithm has been implemented in R software and can be found on the internet.




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Citation

Mello, M. P., Risso, J., Atzberger, C., Aplin, P., Pebesma, E., Vieira, C. A. O., & Rudorff, B. F. T. (2013). Bayesian networks for raster data (BayNeRD): plausible reasoning from observations. Remote Sensing, 5(11), https://doi.org/10.3390/rs5115999

• Journal Article Type: Article
• Publication Date: Nov 15, 2013
• Deposit Date: Apr 30, 2014
• Publicly Available Date: Apr 30, 2014
• Journal: Remote Sensing
• Electronic ISSN: 2072-4292
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 5
• Issue: 11
• DOI: https://doi.org/10.3390/rs5115999
• Public URL: https://nottingham-repository.worktribe.com/output/719418
• Publisher URL: http://www.mdpi.com/2072-4292/5/11/5999