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.
Highlights
Understanding complex phenomena in the field of Earth observation sciences represents a considerable challenge for scientific analysis [1,2]
Some of the main soybean production centers according to IBGE [67] are highlighted on the Probability Image (PI) and allow us to verify the spatial coherence between PI and official soybean statistics
This paper described the basis functioning and implementation of a computer aided BN method for raster data analysis: Bayesian Networks for Raster Data (BayNeRD)
Summary
Understanding complex phenomena in the field of Earth observation sciences represents a considerable challenge for scientific analysis [1,2]. Regarding investigation of large scale phenomena, great progress has been achieved through recent advances in spaceborne remote sensing data acquisition [3], together with the availability of high performance computing for remotely sensed data analysis [4]. Some phenomena are often too complex to be investigated by conventional methods [6], demanding new computer aided methods to help characterize phenomena through plausible reasoning inferences based on consistent data observations (i.e., evidence). The Bayes’ theorem updates the knowledge (prior probability) of a specific event in the light of new/additional evidence (conditional probabilities), allowing one to have a plausible reasoning based on a degree of belief (posteriori probability) [8]. When the number of variables increases or even when the complexity of the interactions among the variables involved in a phenomenon rises, the Bayesian Network (BN) is a representation suited to model and handle such tasks [9,10]
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