Conference Agenda

The estimation of forest biomass in the Amazon is fundamental for supporting climate mitigation policies, both in Brazil and globally, as well as for understanding the significance of existing carbon stocks. This study compares data from the ESA's Biomass satellite, equipped with P-band SAR radar capable of penetrating clouds and dense canopies, with estimates derived from a local algorithm developed by the authors. Quantitative analyses employing RMSE and R² metrics assessed the concordance between the datasets, while discrepancy maps revealed systematic spatial differences.The qualitative assessment demonstrated the advantage of Biomass in cloud-covered regions and in capturing structural details of the forest. The results indicate that, although the local algorithm performs adequately in less dense areas, Biomass provides more reliable estimates in dense forests, such as those of the Brazilian Amazon. The study reinforces the value of integrating radar observations with algorithmic methods for comprehensive monitoring of Amazonian biomass.

Furthermore, the goal of the present analysis is to conduct comparative analyses to generate error metrics between the ESA data and those calculated by the 6BIOS project of the University of Aveiro and the Federal University of Paraíba, seeking to direct this discrepancy toward improving the calibration of biomass calculations acquired by the Biomass satellite. To perform this analysis, we conducted a study focused in a specific area of the Brazilian Amazon rainforest for which prior analyses have been performed, both by satellite and, in the future, enabling comparison with data obtained from regions that have been calculated in situ. The proposal is to demonstrate, in a highly restricted and focused manner, comparative data to advance the calibration and subsequent validation of data obtained from the Biomass satellite. In conclusion, we present suggestions for the need for more in-depth studies regarding coverage errors, such as occlusion, and the potential performance improvement that may be obtained from future measurements by the Biomass satellite.