A simplified Bayesian Network to map soybean plantations

Bayesian Network (BN) techniques can be used to represent the causal relationships among random variables on probabilistic models. Only few studies have applied these techniques to remote sensing and other spatial data integrated in geographic information systems. The objective of the present work was to map soybean plantation using minimum of EVI (M), range of EVI (R) and terrain slope (L) as input variables in the BN. Soybean plantations were evaluated in the state of Rio Grande do Sul, Brazil during the 2000/01 crop year. The probability function was discretized with five different numbers of intervals. Results were improved with the increase of the number of intervals. Best soybean mapping result presented sensitivity, specificity and overall accuracy indices equal to 77.62, 77.56 and 77.58%, respectively, indicating that the method is promising and has potential to be improved with the use of additional input variables.