Title :
A simplified Bayesian Network to map soybean plantations
Author :
Mello, Marcio Pupin ; Rudorff, Bernardo F T ; Adami, Marcos ; Rizzi, Rodrigo ; Aguiar, Daniel A. ; Gusso, Anibal ; Fonseca, Leila M G
Author_Institution :
Nat. Inst. for Space Res. (INPE), São José dos Campos, Brazil
Abstract :
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.
Keywords :
agriculture; belief networks; crops; geographic information systems; geophysical signal processing; probability; vegetation mapping; AD 2000 to 2001; Bayesian network; Brazil; EVI minimum; EVI range; Rio Grande do Sul; causal relationships; enhanced vegetation index; geographic information systems; probabilistic models; probability function; random variables; remote sensing; soybean plantation mapping; terrain slope; Accuracy; Agriculture; Bayesian methods; Input variables; MODIS; Pixel; Sensitivity; MODIS; Remote sensing; agriculture monitoring; artificial intelligence; belief network;
Conference_Titel :
Geoscience and Remote Sensing Symposium (IGARSS), 2010 IEEE International
Conference_Location :
Honolulu, HI
Print_ISBN :
978-1-4244-9565-8
Electronic_ISBN :
2153-6996
DOI :
10.1109/IGARSS.2010.5651814