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The objective of this study was to analyze the L-band SAR backscatter sensitivity to forest biomass for Eucalyptus plantations. The results showed that the radar signal is highly dependent on biomass only for values lower than 50 t/ha, which corresponds to plantations of approximately three years of age. Next, Random Forest regressions were performed to evaluate the potential of PALSAR data to predict the Eucalyptus biomass. Regressions were constructed to link the biomass to both radar signal and age of plantations. Results showed that the age was the variable that best explained the biomass followed by the PALSAR HV polarized signal. For biomasses lower than 50 t/ha, HV signal and plantation age were found to have the same level of importance in predicting biomass. For biomasses higher than 50 t/ha, plantation age was the main variable in the random forest models. The use of PALSAR signal alone did not correctly predict the biomass of Eucalyptus plantations (R² lower than 0.5 and RMSE higher than 46.7 t/ha). The use of plantation age in addition to the PALSAR signal improved slightly the prediction results (R² increased from 0.88 to 0.92 and RMSE decreased from 22.7 to 18.9 t/ha).

The Geoscience Laser Altimeter System (GLAS) has provided a useful dataset for estimating forest height in many areas of the globe. Most of the studies on GLAS waveforms have focused on natural forests and only a few were conducted over forest plantations. The objective of this study was to test the best known models used for estimating canopy height and above ground biomass of intensively managed Eucalyptus plantations in Brazil using full waveform LiDAR data. Studies to estimate forest heights from LiDAR data have highlighted that the fitting coefficients of developed models are strongly dependent on environmental factors such as the region of the study site, terrain topography, and forest type. In this study, we evaluated the main models developed to predict canopy height using a combination of parameters extracted from GLAS waveforms and a digital elevation model, in order to explore which combination of parameters yields the best forest height estimates. In addition, a model to estimate above ground biomass from dominant height was calibrated.