Assessment of dual polarization in Sentinel-1 data for estimating forest aboveground biomass: case study of Barru Regency, South Sulawesi

Abstract

Remote sensing has been widely used in the estimation of forest aboveground biomass (AGB) which is essential for climate change mitigation,by using either optical or radar data and its combination. This estimation of AGB from remote sensing data is now supported by the availability of the freely available dual-polarization Sentinel 1 SAR data. However, the assessment of the accuracy for measuring AGB from the VV and VH polarization in Sentinel-1 data in Indonesia is still limited. This study aims to assess the performance of VV and VH polarization and the combination with texture data from Sentinel-1 for estimating AGB in tropical forest of Barru Regency, South Sulawesi. The AGB was calculated by using backscatter value from C-band SAR dual-polarization and Grey Level of Measure (GLCM) texture data from Sentinel-1 as the independent variables, and ground inventory plots as the dependent variable. Twenty-three plots of field inventory data were collected whereas 16 plots were used in the regression models and the remaining seven plots were used to validate the result. The allometric equation was used to calculate the biomass value of the field survey data then multilinear regression models were generated by using biomass value, backscatter data from VV and VH polarization, and texture data. The performance of the resulted multilinear regression models was compared by looking at the coefficient of determination (R2) and RMSE value using cross-validation. The results demonstrated that combination of VH and GLCM texture suggest as the best to estimate the AGB based on higher value of R2 = 0.44 and SE 83.7 kg/tree. In conclusion, VH polarization usage in vegetation AGB modelling has been able to predict 3 % higher than by using VV polarisation. The inclusion of texture also had been able to increase the model performance by 5 to 7 % which demonstrated the importance of having texture variables in the analysis of AGB.