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Bayesian Networks for Raster Data (BayNeRD): Plausible Reasoning from Observations

Remote Sensing Division (DSR), National Institute for Space Research (INPE), São José dos Campos, SP 12227-010, Brazil
Institute of Surveying, Remote Sensing and Land Information (IVFL), University of Natural Resources and Life Sciences (BOKU), Vienna 1190, Austria
Institute for Geoinformatics (ifgi), University of Muenster, D-48151 Muenster, Germany
Agrosatélite Applied Geotechnology, Florianópolis, SC 88032-005, Brazil
School of Geography, University of Nottingham, Nottingham NG7 2RD, UK
Geosciences Department, Federal University of Santa Catarina (UFSC), Florianópolis, SC 88040-900, Brazil
Author to whom correspondence should be addressed.
Remote Sens. 2013, 5(11), 5999-6025;
Received: 4 September 2013 / Revised: 8 November 2013 / Accepted: 11 November 2013 / Published: 15 November 2013
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. View Full-Text
Keywords: Belief network; weights of evidence; remote sensing; ancillary data; soybean mapping Belief network; weights of evidence; remote sensing; ancillary data; soybean mapping
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MDPI and ACS Style

Mello, M.P.; Risso, J.; Atzberger, C.; Aplin, P.; Pebesma, E.; Vieira, C.A.O.; Rudorff, B.F.T. Bayesian Networks for Raster Data (BayNeRD): Plausible Reasoning from Observations. Remote Sens. 2013, 5, 5999-6025.

AMA Style

Mello MP, Risso J, Atzberger C, Aplin P, Pebesma E, Vieira CAO, Rudorff BFT. Bayesian Networks for Raster Data (BayNeRD): Plausible Reasoning from Observations. Remote Sensing. 2013; 5(11):5999-6025.

Chicago/Turabian Style

Mello, Marcio P., Joel Risso, Clement Atzberger, Paul Aplin, Edzer Pebesma, Carlos A.O. Vieira, and Bernardo F.T. Rudorff 2013. "Bayesian Networks for Raster Data (BayNeRD): Plausible Reasoning from Observations" Remote Sensing 5, no. 11: 5999-6025.

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