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@InProceedings{VasconcellosHenCorSilHud:2019:EsLeAr,
               author = "Vasconcellos, Bruna Nascimento de and Hentz, {\^A}ngela Maria 
                         Klein and Corte, Ana Paula Dalla and Silva, Carlos Alberto and 
                         Hudak, Andrew Thomas",
          affiliation = "{Universidade Federal do Paran{\'a} (UFPR)} and {Universidade 
                         Federal do Paran{\'a} (UFPR)} and {Universidade Federal do 
                         Paran{\'a} (UFPR)} and {University of Maryland} and {US Forest 
                         Service (USDA)}",
                title = "Estimation of leaf area index in a Mixed Ombrophilous Forest using 
                         remote sensing data",
            booktitle = "Anais...",
                 year = "2019",
               editor = "Gherardi, Douglas Francisco Marcolino and Sanches, Ieda DelArco 
                         and Arag{\~a}o, Luiz Eduardo Oliveira e Cruz de",
                pages = "3377--3380",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 19. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "Hemispherical photographs, Mixed Ombrophilous Forest, Pleiades, 
                         Vegetation Index.",
             abstract = "This study was conducted to estimate leaf area index (LAI) in a 
                         fragment of Mixed Ombrophilous Forest, in S{\~a}o Jo{\~a}o do 
                         Triunfo, Brazil, using remote sensing techniques. The LAI was 
                         generated from orbital images from the Pleiades sensor, based on 
                         the relationship with vegetation indices (VI). On the ground, LAI 
                         was estimated from the CI-110 Plant Canopy Analyzer, assumed to 
                         measure actual LAI. The data obtained by the remote sensing 
                         techniques were correlated with the data obtained from the field, 
                         using the Pearson's correlation coefficient. The Normalized 
                         Difference Vegetation Index (NDVI) correlation with LAI (r = 0.32) 
                         was greatly surpassed by the Soil-Adjusted Vegetation Index (SAVI) 
                         correlation with LAI (r = 0.78). Meanwhile, the model proposed by 
                         SEBAL was moderately correlated with LAI (r = 0.66).",
  conference-location = "Santos",
      conference-year = "14-17 abril 2019",
                 isbn = "978-85-17-00097-3",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3U25CP5",
                  url = "http://urlib.net/rep/8JMKD3MGP6W34M/3U25CP5",
           targetfile = "97376.pdf",
                 type = "Floresta e outros tipos de vegeta{\c{c}}{\~a}o",
        urlaccessdate = "25 jan. 2021"
}


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