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@Article{AlmeidaGAOJPSLGSFL:2019:CoLiHy,
               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 Lopes, Aline 
                         Pontes and Gra{\c{c}}a, Paulo Maur{\'{\i}}cio Lima de 
                         Alencastro 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 {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas da Amaz{\^o}nia (INPA)} and {Lancaster University} and 
                         {Instituto de Desenvolvimento Sustent{\'a}vel Mamirau{\'a}} and 
                         {California Institute of Technology}",
                title = "Combining LiDAR and hyperspectral data for aboveground biomass 
                         modeling in the Brazilian Amazon using different regression 
                         algorithms",
              journal = "Remote Sensing of Environment",
                 year = "2019",
               volume = "232",
                pages = "e111323",
                month = "Oct.",
             keywords = "Hyperspectral remote sensing, Laser scanning, Data integration, 
                         Tropical forest, Carbon stock.",
             abstract = "Accurate estimates of aboveground biomass (AGB) in tropical 
                         forests are critical for supporting strategies of ecosystem 
                         functioning conservation and climate change mitigation. However, 
                         such estimates at regional and local scales are still highly 
                         uncertain. Airborne Light Detection And Ranging (LiDAR) and 
                         Hyperspectral Imaging (HSI) can characterize the structural and 
                         functional diversity of forests with high accuracy at a sub-meter 
                         resolution, and potentially improve the AGB estimations. In this 
                         study, we compared the ability of different data sources (airborne 
                         LiDAR and HSI, and their combination) and regression methods 
                         (linear model - LM, linear model with ridge regularization - LMR, 
                         Support Vector Regression - SVR, Random Forest - RF, Stochastic 
                         Gradient Boosting - SGB, and Cubist - CB) to improve AGB 
                         predictions in the Brazilian Amazon. We used georeferenced 
                         inventory data from 132 sample plots to obtain a reference field 
                         AGB and calculated 333 metrics (45 from LiDAR and 288 from HSI) 
                         that could be used as predictors for statistical AGB models. We 
                         submitted the metrics to a correlation filtering followed by a 
                         feature selection procedure (recursive feature elimination) to 
                         optimize the performance of the models and to reduce their 
                         complexity. Results showed that both LiDAR and HSI data used alone 
                         provided relatively high accurate models if adequate metrics and 
                         algorithms are chosen (RMSE = 67.6 Mg.ha\−1 , RMSE% = 36%, 
                         R2 = 0.58, for the best LiDAR model; RMSE = 68.1 Mg.ha\−1 , 
                         RMSE % = 36%, R2 = 0.58, for the best HSI model). However, 
                         HSI-only models required more metrics (512) than LiDAR-only models 
                         (25). Models combining metrics from both datasets resulted in more 
                         accurate AGB estimates, regardless of the regression method (RMSE 
                         = 57.7 Mg.ha\−1 , RMSE% = 31%, R2 = 0.70, for the best 
                         model). The most important LiDAR metrics for estimating AGB were 
                         related to the upper canopy cover and tree height percentiles, 
                         while the most important HSI metrics were associated with the near 
                         infrared and shortwave infrared spectral regions, particularly the 
                         leaf/canopy water and lignin-cellulose absorption bands. Finally, 
                         an analysis of variance (ANOVA) showed that the remote sensing 
                         data source (LiDAR, HSI, or their combination) had a greater 
                         effect size than the regression algorithms. Thus, no single 
                         algorithm outperformed the others, although the LM method was less 
                         suitable when applied to the HSI and hybrid datasets. Results show 
                         that the synergistic use of LiDAR and hyperspectral data has great 
                         potential for improving the accuracy of the biomass estimates in 
                         the Brazilian Amazon.",
                  doi = "10.1016/j.rse.2019.111323",
                  url = "http://dx.doi.org/10.1016/j.rse.2019.111323",
                 issn = "0034-4257",
             language = "en",
           targetfile = "almeida_combining.pdf",
        urlaccessdate = "12 abr. 2021"
}


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