@InProceedings{SeixasFiorPoleStra:2017:VaMoEs,
               author = "Seixas, Hugo Tameir{\~a}o and Fiorio, Peterson Ricardo and Polez, 
                         Bruna Mariani and Strabeli, Taila Fernanda",
                title = "Valida{\c{c}}{\~a}o de modelos espectrais para a 
                         predi{\c{c}}{\~a}o de conte{\'u}do relativo de {\'a}gua (CRA) 
                         em folhas de Eucalyptus spp",
            booktitle = "Anais...",
                 year = "2017",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "6741--6748",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 18. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "There will be an increasing demand for wood products in the next 
                         years, being necessary to develop new technologies to improve the 
                         efficiency of the forestry production. The water status of the 
                         plant is an important factor in the productivity, and can have 
                         great impacts on the culture. Remote sensing can be considered as 
                         a useful tool to measure water content of leaves, being applicable 
                         over a variety of scales. The objective of this study was to 
                         evaluate the efficiency of three spectral models over their 
                         capacity of predicting relative water content of Eucalyptus spp. 
                         leaves. The water content data was obtained through gravimetric 
                         analysis of fresh, saturated and dry leaves, and by hyperspectral 
                         measures in laboratory. This methodology found the average values 
                         of relative water content was similar between the observed data 
                         and the estimated data from the stepwise model, however, it showed 
                         a big difference when compared to the single band and spectrum 
                         regions models. However, none of the data generated by the models 
                         presented significant correlation with the observed RWC values, 
                         which the stepwise model showed the highest coefficient of 
                         determination (R2=0.012) and the single band and spectrum regions 
                         the lowest (R2=0.004) and (R2=0.002) respectively. The results 
                         indicate that these models couldnt predict relative water content 
                         values to individual leaves, but the average RWC obtained by the 
                         stepwise model can be considered similar to the observed RWC.",
  conference-location = "Santos",
      conference-year = "28-31 maio 2017",
                 isbn = "978-85-17-00088-1",
                label = "60174",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3PSMDF4",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3PSMDF4",
           targetfile = "60174.pdf",
                 type = "Radiometria e sensores",
        urlaccessdate = "27 abr. 2024"
}