@InProceedings{AlmeidaGaArOmJaPeSa:2019:SeHyVa,
               author = "Almeida, Catherine Torres de and Galv{\~a}o, L{\^e}nio Soares 
                         and Arag{\~a}o, Luiz Eduardo Oliveira e Cruz de and Ometto, Jean 
                         Pierre Henry Balbaud and Jacon, Aline Daniele and Pereira, 
                         Francisca Rocha de Souza and Sato, Luciane Yumie",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)} and {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Selection of hyperspectral variables for aboveground biomass 
                         estimation in the Brazilian Amazon",
                 year = "2019",
         organization = "Congresso Mundial da IUFRO",
             abstract = "Due to the limited coverage of field Aboveground Biomass (AGB), 
                         remote sensing becomes an alternative for monitoring carbon stocks 
                         at the landscape scale. However, the most commonly used sensors 
                         have limited spectral resolution. Hyperspectral imaging (HSI) 
                         provides high-resolution information, although its high data 
                         dimensionality becomes a challenge for modeling. In this context, 
                         selection of suitable variables is a critical step for estimating 
                         AGB from HSI data. Support Vector Regression coupled with the 
                         Recursive Feature Elimination approach (SVR-RFE) can produce 
                         parsimonious models from a reduced subset of features. We applied 
                         the SVR-RFE in a 5-fold cross-validation strategy with 5 
                         repetitions to determine which hyperspectral variables were most 
                         effective to estimate AGB. We used field AGB from 147 inventory 
                         plots across the Brazilian Amazon and 64 plot-level HSI metrics, 
                         including 14 reflectance bands, 30 vegetation indices, 
                         continuum-removal absorption features at five wavelengths (495, 
                         670, 980, 1200, and 2100 nm), and endmember fractions (green 
                         vegetation, shade, and non-photosynthetic vegetation/soil) from 
                         Spectral Mixture Analysis. The SVR-RFE explained 67% of the AGB 
                         variation, by selecting eight HSI variables. The three most 
                         effective variables came from the shortwave infrared region (width 
                         and depth of the 2100-nm absorption band and the NDNI index), 
                         related to canopy moisture and lignin-cellulose-nitrogen 
                         absorption bands. Four metrics were retrieved from the water 
                         absorption band centered at 980 nm (depth, asymmetry, and the 
                         indices PWI and LWVI1). The width of the band placed at 495 nm was 
                         also selected. SVR-RFE proved to be an efficient technique for 
                         estimating AGB from HSI data.",
  conference-location = "Curitiba, PR",
      conference-year = "29 set. - 05 out.",
             language = "en",
        urlaccessdate = "01 maio 2024"
}