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@Article{FerreiraGronRoliShim:2013:AnSpVa,
               author = "Ferreira, Matheus Pinheiro and Grondona, Atilio Efrain Bica and 
                         Rolim, Silvia Beatriz Alves and Shimabukuro, Yosio Edemir",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and Univ Fed 
                         Rio Grande do Sul, Ctr Remote Sensing, BR-91501970 Porto Alegre, 
                         RS, Brazil. and Univ Fed Rio Grande do Sul, Ctr Remote Sensing, 
                         BR-91501970 Porto Alegre, RS, Brazil. and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Analyzing the spectral variability of tropical tree species using 
                         hyperspectral feature selection and leaf optical modeling",
              journal = "Journal of Applied Remote Sensing",
                 year = "2013",
               volume = "7",
               number = "1",
                pages = "073502",
                month = "Oct.",
             keywords = "species discrimination, tropical forest, Atlantic Forest, 
                         spectroscopy, PROSPECT-5.",
             abstract = "Hyperspectral remote sensing can provide information about species 
                         richness over large areas and may be useful for species 
                         discrimination in tropical environments. Here, we analyze the main 
                         sources of variability in leaf spectral signatures of tropical 
                         trees and examine the potential of spectroscopic reflectance 
                         measurements (450 to 2450 nm) for tree species discrimination. We 
                         assess within-and among-species spectral variability and perform a 
                         feature selection procedure to identify wavebands in which the 
                         species most differ from each other. We assess the discriminative 
                         power of these wavebands by calculating a separability index and 
                         then classifying the species. Finally, leaf chemical and 
                         structural parameters of each species are retrieved by inversion 
                         of the leaf optical model PROSPECT-5. Among-species spectral 
                         variability is almost five times greater than within-species 
                         spectral variability. The feature selection procedure reveals that 
                         wavebands, where species most differ, are located at the visible, 
                         red edge, and shortwave infrared regions. Classification of the 
                         species using these wavebands reaches 96\\% overall accuracy. 
                         Leaf chemical and structural properties retrieve by model 
                         inversion show that differences in leaf pigment concentrations and 
                         leaf internal structure are the most important parameters 
                         controlling the spectral variability of species. These parameters 
                         also contribute to the variation in red edge position among 
                         species.",
                  doi = "10.1117/1.JRS.7.073502",
                  url = "http://dx.doi.org/10.1117/1.JRS.7.073502",
                 issn = "1931-3195",
                label = "isi 2013-11",
             language = "en",
           targetfile = "JARS_7_1_073502.pdf",
        urlaccessdate = "23 jan. 2021"
}


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