@InProceedings{AlmeidaGaArOmJaPeSa:2019:CoReTe,
               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 = "Comparison of regression techniques for LiDAR-derived aboveground 
                         biomass estimation in the Amazon",
                 year = "2019",
         organization = "Congresso Mundial da IUFRO",
             abstract = "Light Detection And Ranging (LiDAR) is an active remote sensor 
                         that has been successfully applied for characterizing canopy 
                         structure, especially to estimate aboveground biomass (AGB). 
                         Parametric models, mainly the linear regression with stepwise 
                         feature selection (LMstep), are the most common approaches used 
                         for estimating AGB. However, non-parametric machine learning 
                         techniques, such as Support Vector Regression (SVR), Stochastic 
                         Gradient Boosting (SGB), and Random Forest (RF), can better 
                         address complex relationships between biomass and remote sensing 
                         variables. Therefore, it is desirable to assess the performance of 
                         different regression strategies. This study aims to compare eight 
                         regression techniques for LiDAR-based AGB estimation: LMstep, 
                         Linear Models with Regularization (LMR), Partial Least Squares 
                         (PLS), K-Nearest Neighbor (KNN), SVR, RF, SGB, and Cubist. For 
                         this purpose, 34 LiDAR metrics were regressed against AGB from 147 
                         inventory plots across the Brazilian Amazon Biome. Models 
                         performance were evaluated by the average Root Mean Squared Error 
                         (RMSE) and R2 from a 5-fold cross-validation strategy with 10 
                         repetitions. The Kruskal-Wallis test was used to evaluate 
                         statistical differences among models. Results showed that LMstep 
                         presented the highest RMSE (68.85 Mg.ha-1) and lowest R2 (0.66), 
                         while SVR had the lowest RMSE (65.23 Mg.ha-1) and highest R2 
                         (0.69). However, the differences in performance of the models were 
                         not statistically significant. Thus, we confirmed the results of 
                         previous studies that showed that simple approaches, such as 
                         linear regression models, performed just as well as advanced 
                         machine learning methods for estimating AGB based on LiDAR data.",
  conference-location = "Curitiba, PR",
      conference-year = "29 set. - 05 out.",
             language = "en",
        urlaccessdate = "01 maio 2024"
}