01813nam a2200301 a 450000100080000000500110000800800410001902000220006010000220008224500910010426002250019530000140042050000210043452007540045565000260120965000190123565000140125465000250126865300240129365300350131765300160135265300160136865300180138465300250140265300340142770000240146170000260148518981772020-01-24       2011    bl uuuu     u00u1 u    #d  a978-80-904830-3-31 aANTUNES, J. F. G.  aData mining for sugarcane crop classification using MODIS data.h[electronic resource]  aIn: EUROPEAN FEDERATION FOR INFORMATION TECHNOLOGY IN AGRICULTURE, FOOD; THE ENVIRONMENT WORLD CONGRESS ON COMPUTERS IN AGRICULTURE, 8., 2011, Prague. Proceedings... Prague: Czech University of Life Sciences Praguec2011  ap. 55-66.  aEFITA/WCCA 2011.  aMODIS (Moderate Resolution Imaging Spectroradiometer) data provide coverage of large areas and high periodicity. These characteristics are fundamental for monitoring strategic agricultural crops in Brazil, such as sugarcane. Data mining is a promising approach to enhance remote sensing data analysis. The objective of this work was to apply the decision tree induction technique to classify sugarcane crop in São Paulo, Brazil, by using MODIS data. A classification model with a good accuracy has been obtained. Furthermore, such an induction technique allowed for knowledge discovery through decision rules that are relevant for specialists. The results revealed the adherence of data mining techniques to satellite image classification problems.  aInformation retrieval  aRemote sensing  aSugarcane  aSensoriamento Remoto  aÁrvore de decisão  aCana-de-açúcar em São Paulo  aDados MODIS  aData mining  aDecision tree  aMineração de dados  aRecuperação da informação1 aRODRIGUES, L. H. A.1 aOLIVEIRA, S. R. de M.