The paper entitled Large Area Mapping of Boreal Growing Stock Volume on an Annual and Multi-Temporal Level Using PALSAR L-Band Backscatter Mosaics has ben published in the open access journal MDPI Forests.
This study presents an application-oriented approach to derive aboveground growing stock volume (GSV) maps using the annual large-area L-band backscatter mosaics provided by the Japan Aerospace Exploration Agency (JAXA). Furthermore, a multi-temporal map has been created to improve GSV estimation accuracy. Based on information from Russian forest inventory data, the maps were generated using the machine learning algorithm, RandomForest.