Estimating Above-Ground Biomass in Finnish Forests Using Remote Sensing Data

Above-ground biomass (AGB) estimation is an important tool for predicting carbon flux and the effects of global warming. This study describes a novel application of remote-sensing based AGB estimation in the hemi-boreal vegetation zone of Finland, using Sentinel-1, Sentinel-2, ALOS-2 PALSAR-2, and the Multi-Source National Forest Inventory by Natural Resources Institute Finland as sources of data. A novel method of extracting data from the features of the surrounding observations is proposed, and the method’s effectiveness was evaluated. The findings showed that the method showed promising results, with the model trained using the extracted features achieving the highest evaluation scores in the study. In addition, the viability of using free and highly available satellite datasets for AGB estimation in the hemi-boreal Finland was analyzed, with the results suggesting that the free Synthetic Aperture Radar (SAR) based products had a low performance. The features extracted from the optical data of Sentinel-2 produced well-performing models, although the accuracy might still be too low to be feasible