@Article{DinizGamReiOliMar:2023:PlBrUs,
               author = "Diniz, Juliana Maria Ferreira de Souza and Gama, F{\'a}bio Furlan 
                         and Reis, Aliny Aparecida dos and Oliveira, Cleber Gonzales de and 
                         Marques, Eduardo Resende Girardi",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Universidade Estadual 
                         de Campinas (UNICAMP)} and {VISIONA Tecnologia Espacial} and 
                         {KLABIN S.A.}",
                title = "Estimating stem volume of Eucalyptus sp. and Pinus sp. plantations 
                         in Brazil, using Sentinel-1B and ALOS-2/PALSAR-2 data",
              journal = "Journal of Applied Remote Sensing",
                 year = "2023",
               volume = "17",
               number = "1",
                pages = "e014513",
                month = "Jan.",
             keywords = "machine learning, multifrequency, polarimetry, synthetic aperture 
                         radar.",
             abstract = "Multifrequency synthetic aperture radar (SAR) data have been 
                         applied to discriminate subtle differences in the vegetation and 
                         to better characterize its structural properties, since each SAR 
                         frequency will interact with the different sections of the 
                         vegetation canopy. In this study, our main objective was to 
                         evaluate the use of multifrequency Sentinel-1 and ALOS-2/PALSAR-2 
                         data for stem volume estimations in Eucalyptus sp. and Pinus sp. 
                         plantations using three different machine learning algorithms: 
                         random forest (RF), support vector regression (SVR), and extreme 
                         gradient boosting (XGB). Different experiments were carried out 
                         using combinations of predictor variables derived from both SAR 
                         sensors: backscattering, polarimetric decompositions, and 
                         interferometry data, and field data considering specific models 
                         for Eucalyptus sp. and Pinus sp. and a generic model comprising 
                         all forest plantations data. The machine learning models using 
                         predictor variables derived from SAR data achieved moderately high 
                         accuracy to predict stem volume, mainly when SAR data were used in 
                         combination with stand age (Experiment iv). In the best prediction 
                         scenario (Experiment iv), the RF, SVR, and XGB models were able to 
                         explain 81.7%, 68.5%, and 81.8% [coefficient of variation (R2) 
                         values] of stem volume variability considering the generic models, 
                         respectively. Our results pointed out that the RF algorithm showed 
                         the best performance in predicting stem volume with significant 
                         good results and easier implementation in comparison with the 
                         other two algorithms (SVR and XGB).",
                  doi = "10.1117/1.JRS.17.014513",
                  url = "http://dx.doi.org/10.1117/1.JRS.17.014513",
                 issn = "1931-3195",
             language = "en",
           targetfile = "014513_1.pdf",
        urlaccessdate = "20 maio 2024"
}