@InProceedings{PereiraCasQuiCaeVel:2015:EsÍnÁr,
               author = "Pereira, Rodrigo Moura and Casaroli, Derblai and Quirino, Dayanna 
                         Teodoro and Caetano, Jordana Moura and Vellame, Lucas Melo",
                title = "Estimativa do {\'{\i}}ndice de {\'a}rea foliar da 
                         cana-de-a{\c{c}}{\'u}car a partir de imagens do sat{\'e}lite 
                         Landsat-8 (OLI)",
            booktitle = "Anais...",
                 year = "2015",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "5772--5779",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 17. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "Leaf Area Index (LAI) is an important variable to monitor of crops 
                         growth, which can be estimated from remote sensing data. This work 
                         evaluated the temporal variation of leaf area index (LAI) by three 
                         different mathematical functions that use NDVI (Normalized 
                         Difference Vegetation Index) derived from Landsat-8 OLI data in 
                         sugarcane area planted with the variety CTC-4, localized in Santo 
                         Antonio of Goias, Goias State, Brazil, to 2013 and 2014 years. 
                         Number of green leaves and leaf area index (LAI) were determined, 
                         in field, through eleven samplings, during 510 days of cane-planta 
                         cycle. In the sugarcane growing season NDVI data ranged from 0 to 
                         0,57. Different LAI values were observed for each LAI computing 
                         method; Method 1 had good performance to estimate LAI whereas 
                         method 2 and 3 underestimates LAI values compared with the field 
                         data. Its possible to estimate the leaf area index of sugarcane in 
                         the cane-planta cycle from the NDVI derived from Landsat-8 data, 
                         but, for the estimates of LAI using remote sensing data it is 
                         necessary to proceed in the estimation and calibration of tuning 
                         parameters considering the environmental and varietal variability 
                         of sugarcane at the field.",
  conference-location = "Jo{\~a}o Pessoa",
      conference-year = "25-29 abr. 2015",
                 isbn = "978-85-17-0076-8",
                label = "1183",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3JM4EK9",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3JM4EK9",
           targetfile = "p1183.pdf",
                 type = "Produ{\c{c}}{\~a}o e previs{\~a}o agr{\'{\i}}cola",
        urlaccessdate = "18 jun. 2024"
}