Multi-sensor prediction of Eucalyptus stand volume: A support vector approach

Multi-Sensor Prediction of Stand Volume by a Hybrid Model of Support Vector Machine for Regression Kriging

“…It was a pioneering study that built the SVRK hybrid model and utilized it to map stand volume. The optimal RBF-kernel SVR model trained in this study as the first step achieved higher accuracy than multiple linear regressions and SVR models with various kernels based on similar multi-sensor satellite data [31,32], while the SVR model in this study was less accurate than that built by ALOS optical and SAR variables [20]. It was attributed to coarser spatial resolution of L band SAR data and the complex composition of tree species in the study area.…”

“…Among the various machine learning techniques, support vector machine is acclaimed for its capacity of dealing with small training datasets in remote sensing-based classification [17,18]. After the re-design to predict quantitative outputs and solve regression problems, this algorithm came to be the support vector machine for regression (SVR) and acquired wide successes in stand volume modeling [19,20]. Hybrid approaches involve either the statistical regression or machine learning model between the target variable and remote sensing predictors, interpolating residuals of predictions by kriging, and combining them [21][22][23].…”

Applications of Sar Data to Estimate Forest Biophysical Variables in Brazil

“…observed that the use of plantation age along with SAR data slightly improved the results of forest biomass estimate, suggesting, therefore, the use of temporal series to predict this variable. Souza et al (2019) tested support vector approach to estimate wood volume in a commercial Eucalyptus grandis plantation in eastern Minas Gerais State, and found that the Radis Basis Function was the most suitable kernel function for model development. The Support Vector Regression (SVR) model allowed the wood volume of eucalyptus plantations to be accurately predicted.…”

“…Each type of vegetation has a specific structure that will interact differently with SAR microwaves, when identifying different succession stages of the Amazon Forest (SANTOS and GONÇALVES, 2009). The polarization of the SAR signal, which refers to the orientation of the electric field emitted and received by the SAR sensor in the vertical (V) or horizontal (H) axis, and can be co-polarized (VV and HH -emitted and received vertically or horizontally, respectively), or cross-polarized (VH, and HV -emitted in one orientation and received in another), will have a great effect on the signal backscattered by vegetation (SOUZA et al, 2019). When additional information from backscattering phase is available, the magnitude of the polarimetric response makes possible the characterization of the objects scattering mechanisms as well (GARCIA et al, 2012).…”

MULTILEVEL NONLINEAR MIXED-EFFECTS MODEL AND MACHINE LEARNING FOR PREDICTING THE VOLUME OF Eucalyptus SPP. TREES

“…Initially, SVM techniques were successfully applied as data classification methods (Cherkassky and Mulier, 1998;Yu et al, 2019). Subsequently, they were expanded to regression tasks using the following approaches: support vector regression (SVR) and leastsquares support vector machines (LS-SVMs) (Karthik et al, 2016;Zeng et al, 2018;Souza et al, 2019;Sivasankar et al, 2019).…”