Eucalyptus Biomass and Volume Estimation Using Interferometric and Polarimetric SAR Data
AbstractThis work aims to establish a relationship between volume and biomass with interferometric and radiometric SAR (Synthetic Aperture Radar) response from planted Eucalyptus saligna forest stands, using multi-variable regression techniques. X and P band SAR images from the airborne OrbiSAR-1 sensor, were acquired at the study area in the southeast region of Brazil. The interferometric height (Hint = difference between interferometric digital elevation model in X and P bands), contributed to the models developed due to fact that Eucalyptus forest is composed of individuals whose structure is predominantly cylindrical and vertically oriented, and whose tree heights have great correlation with volume and biomass. The volume model showed that the stand volume was highly correlated with the interferometric height logarithm (Log10Hint), since Eucalyptus tree volume has a linear relationship with the vegetation height. The biomass model showed that the combination of both Hint2 and Canopy Scattering Index—CSI (relation of s°VV by the sum of s°VV and s°HH, which represents to the canopy interaction) were used in this model, due to the fact that the Eucalyptus biomass and the trees height relationship is not linear. Both models showed a prediction error of around 10% to estimate the Eucalyptus biomass and volume, which represents a great potential to use this kind of technology to help establish Eucalyptus forest inventory for large areas.
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Gama, F.F.; Dos Santos, J.R.; Mura, J.C. Eucalyptus Biomass and Volume Estimation Using Interferometric and Polarimetric SAR Data. Remote Sens. 2010, 2, 939-956.
Gama FF, Dos Santos JR, Mura JC. Eucalyptus Biomass and Volume Estimation Using Interferometric and Polarimetric SAR Data. Remote Sensing. 2010; 2(4):939-956.Chicago/Turabian Style
Gama, Fábio Furlan; Dos Santos, João Roberto; Mura, José Claudio. 2010. "Eucalyptus Biomass and Volume Estimation Using Interferometric and Polarimetric SAR Data." Remote Sens. 2, no. 4: 939-956.