Estimation of Boreal Forest Growing Stock Volume in Russia from Sentinel-2 MSI and Land Cover Classification

Growing stock volume (GSV) is a fundamental parameter of forests, closely related to the above-ground biomass and hence to carbon storage. Estimation of GSV at regional to global scales depends on the use of satellite remote sensing data, although accuracies are generally lower over the sparse boreal forest. This is especially true of boreal forest in Russia, for which knowledge of GSV is currently poor despite its global importance. Here we develop a new empirical method in which the primary remote sensing data source is a single summer Sentinel-2 MSI image, augmented by land-cover classification based on the same MSI image trained using MODIS-derived data. In our work the method is calibrated and validated using an extensive set of field measurements from two contrasting regions of the Russian arctic. Results show that GSV can be estimated with an RMS uncertainty of approximately 35–55%, comparable to other spaceborne estimates of low-GSV forest areas, with 70% spatial correspondence between our GSV maps and existing products derived from MODIS data. Our empirical approach requires somewhat laborious data collection when used for upscaling from field data, but could also be used to downscale global data

Regional monitoring of forests using the Vega-Les system: case study for Tungussko-Chunskoye forest management unit and Tunguska reserve in the Russian Krasnoyarsk region

This paper demonstrates the capabilities of the Vega-Les (“Les” is the Russian word meaning “forest”) information system (IS) for forest monitoring. A brief assessment and characteristics of the Earth observation data and main available thematic products about Russian forests available in the system are given. An assessment of the capabilities of the Vega-Les IS for studying local scale forest changes was carried out. The Tungussko-Chunskoye forest management unit (FMU) and the Tunguska nature reserve in the Russian Krasnoyarsk Krai region were chosen as the test area. The analysis of forest cover changes over this area since the beginning of the 21st century, including the changes in the number and extent of wildfires, is presented. As a result, it is concluded that the Vega-Les IS is applicable for remote assessment and monitoring of various characteristics of Russian forests