@InProceedings{AlmeidaGAOJPSSFL:2019:AbBiEs,
               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 and Silva, Camila 
                         Val{\'e}ria de Jesus and Ferreira Ferreira, Jefferson and Longo, 
                         Marcos",
          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)} and {Lancaster 
                         University} and {Instituto de Desenvolvimento Sustent{\'a}vel 
                         Mamirau{\'a}} and {Jet Propulsion Laboratory}",
                title = "Aboveground biomass estimation in the brazilian Amazon using 
                         combined LIDAR and hyperspectral 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 = "1843--1846",
         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 = "imaging spectrometry, laser scanning, machine learning, biomass, 
                         tropical forest.",
             abstract = "Active Light Detection And Ranging (LiDAR) and passive 
                         Hyperspectral Imaging (HSI) remote sensing provide complementary 
                         information that can be combined to improve the estimation of 
                         vegetation properties, such as aboveground biomass (AGB). Thus, 
                         the main objective of this study is to evaluate the combined use 
                         of LiDAR and HSI data for estimating AGB in the Brazilian Amazon, 
                         by using six regression methods, a high range of remote sensing 
                         metrics, and feature selection. To assess the prediction ability 
                         of the remote sensing data, single and combined LiDAR and HSI 
                         metrics were regressed against AGB from 147 sample plots across 
                         the Brazilian Amazon Biome. Overall, the results showed a similar 
                         model performance for both LiDAR and HSI single datasets, and for 
                         the regression methods used. However, the combination of LiDAR and 
                         HSI data improved the AGB estimation accuracy.",
  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/3U255GH",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3U255GH",
           targetfile = "97359.pdf",
                 type = "Floresta e outros tipos de vegeta{\c{c}}{\~a}o",
        urlaccessdate = "20 maio 2024"
}