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In the field of forestry studies, microwave remote sensing has broad applications due to the penetration into the semi-transparent media. This feature is used for the estimation of biophysical parameters and monitoring of deforestation. Therefore, the estimation of biophysical parameters is essential for assessing carbon stock management. Hence, the aboveground biomass (AGB) using synthetic aperture radar (SAR) data is recognized as typical approaches in forest application. However, the integrated use of polarimetric (PolSAR) and interferometric (PolInSAR) data might be more efficient tools for AGB mapping. Accordingly, in this study with the integrated data, the efficiency of machine learning techniques including random forest regression (RFR) and multiple linear regression (MLR) model were assessed and compared for the prediction of AGB. The analyses were performed using an image pair of fully polarimetric Radarsat-2 C-band data set and the related field data of Malhan Forest Range, Dehradun Forest Division, which were collected using the systematic sampling technique. Particularly, the training and testing of the models were done using the field sample plots. The experimental results showed that the RFR algorithm provided a better prediction result of AGB than the MLR model. The correlation coefficient (R2) and root-mean-square error (RMSE) for the RFR algorithm was estimated to be around 0.65 and 24.33 Mg/ha, respectively, while for the MLR model, R2 and RMSE are estimated as 0.54 and 33.05 Mg/ha, respectively. Therefore, it was concluded that the prediction of AGB through the machine learning technique using PolSAR and PolInSAR data has a significant advantage for accurate estimation of the AGB. [ABSTRACT FROM AUTHOR]

Forest plays an important role in carbon sequestration and biosphere-atmosphere interaction. Knowledge of forest biomass content helps in assessing its sustainability and thus mitigating climate change. The advancement in remote sensing technology provides the capability of estimating biomass at a large scale. Hybrid polarimetry has gained significant attention among other radar missions due to its fundamental advantages. In this article, the potential of hybrid polarimetric SAR is evaluated for the efficient forest aboveground biomass (AGB) estimation for Barkot Forest, Uttarakhand, India. Forest biomass is calculated by means of the extended water cloud model. Scattering parameters are derived using two widely used hybrid polarimetric decomposition techniques, m−δ and m–χ decompositions. Potential insight into the efficacy of these decomposition techniques toward biomass estimation is brought forth. The modeled AGB estimates were compared with fully polarimetric data-based estimated AGB. The estimation based on the m–χ and m−δ decomposition resulted in biomass estimation with an accuracy of 75.8% and 73.4%, respectively. [ABSTRACT FROM AUTHOR]