@Article{SouzaSLGAFSSVDS:2019:SuVeAp,
               author = "Souza, Guilherme Silverio Aquino de and Soares, Vicente Paulo and 
                         Leite, Helio Garcia and Gleriani, Jos{\'e} Marinaldo and Amaral, 
                         Cibele Hummel do and Ferraz, Ant{\^o}nio Santana and Silveira, 
                         Marcus Vinicius de Freitas and Santos, Jo{\~a}o Fl{\'a}vio Costa 
                         dos and Velloso, Sidney Geraldo Silveira and Domingues, Getulio 
                         Fonseca and Silva, Simone",
          affiliation = "{Universidade Federal de Vi{\c{c}}osa (UFV)} and {Universidade 
                         Federal de Vi{\c{c}}osa (UFV)} and {Universidade Federal de 
                         Vi{\c{c}}osa (UFV)} and {Universidade Federal de Vi{\c{c}}osa 
                         (UFV)} and {Universidade Federal de Vi{\c{c}}osa (UFV)} and 
                         {Universidade Federal de Vi{\c{c}}osa (UFV)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Universidade Federal 
                         de Vi{\c{c}}osa (UFV)} and {Instituto Brasileiro de Geografia e 
                         Estat{\'{\i}}stica (IBGE)} and {Universidade Federal de 
                         Vi{\c{c}}osa (UFV)} and {Universidade Federal de Vi{\c{c}}osa 
                         (UFV)}",
                title = "Multi-sensor prediction of Eucalyptus stand volume: a support 
                         vector approach",
              journal = "ISPRS Journal of Photogrammetry and Remote Sensing",
                 year = "2019",
               volume = "156",
                pages = "135--146",
                month = "Oct.",
             keywords = "ALOS AVNIR-2, ALOS PALSAR, Machine learning, Monte Carlo 
                         cross-validation, Sampling intensity, L-band, Synthetic aperture 
                         radar.",
             abstract = "Stem volume is a key attribute of Eucalyptus forest plantations 
                         upon which decision-making is based at diverse levels of planning. 
                         Quantifying volume through remote sensing can support a proper 
                         management of forests. Because of limitations on spaceborne 
                         optical and synthetic aperture radar sensors, this study 
                         integrated both types of datasets assembled using support vector 
                         regression (SVR) to retrieve the stand volume of Eucalyptus 
                         plantations. We assessed different combinations of sensors and a 
                         minimum number of plots to develop an SVR model. Finally, the best 
                         SVR performance was compared with other analytical methods already 
                         tested and in the literature: multilinear regression, artificial 
                         neural networks (ANN), and random forest (RF). Here, we introduce 
                         a test for comparative analysis of the performance of different 
                         methods. We found that SVR accurately predicted stem volume of 
                         Brazilian fast-growing Eucalyptus forest plantations. Gaussian 
                         radial basis was the most suitable kernel function. Integrating 
                         the optical and L-band backscatter data increased the predictive 
                         accuracy compared to a single sensor model. Combining NIR-band 
                         data from ALOS AVNIR-2 and backscatter of L-band horizontal 
                         emitted and vertical received (HV) electric fields from ALOS 
                         PALSAR produced the most accurate SVR model (with an R2 of 0.926 
                         and root mean square error of 11.007 m3 /ha). The number of field 
                         plots sufficient for model development with non-redundant 
                         explanatory variables was 77. Under this condition, SVR performed 
                         similarly to ANN and outperformed the multiple linear regression 
                         and random forest methods.",
                  doi = "10.1016/j.isprsjprs.2019.08.002",
                  url = "http://dx.doi.org/10.1016/j.isprsjprs.2019.08.002",
                 issn = "0924-2716",
             language = "en",
           targetfile = "souza_multi.pdf",
        urlaccessdate = "27 abr. 2024"
}