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

Abstract

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.

Boreal Forest Height Mapping using Sentinel-1 Time Series and improved LSTM model

“…Observed experimental results are encouraging further investigations and are generally in line with other reported studies in boreal forest biome [3], [7], [48], [49]. Obtained accuracies are notably higher than in several other studies using Sentinel-1 data or Sentinel-2 datasets or their combinations, and compare well versus earlier multisensor EO data studies [3], [20], [48]- [52].…”

Stand Biomass at Treeline Ecotone in Russian Subarctic Mountains Is Primarily Related to Species Composition but Its Dynamics Driven by Improvement of Climatic Conditions

“…For this reason, the biomass of treeline ecotone stands was mainly studied on one mountain slope [8,[18][19][20][21][22], but the interregional comparison of such data was done only in one study [23]. In the last years, remote sensing methods have increasingly been used to try to estimate vegetation biomass [10,24]. However, the resolution of satellite images is still too coarse to assess year-by-year biomass changes, and the use of UAV aerial photos or Li-DAR survey data requires ground validation [25][26][27] through time-consuming tree morphometry estimates.…”

“…Thus, detailed research of biomass of tree stands at the upper limit of their growth is strongly needed to improve our understanding of C cycling in high elevation ecosystems, as well as for forecasting how these systems will be transformed by climate change. In the last years, remote sensing methods have increasingly been used to try to estimate vegetation biomass [10,24]. However, the resolution of satellite images is still too coarse to assess year-by-year biomass changes, and the use of UAV aerial photos or LiDAR survey data requires ground validation [25][26][27] through time-consuming tree morphometry estimates.…”

Wall-to-Wall Mapping of Forest Biomass and Wood Volume Increment in Italy

“…The spatial detail over small areas can be achieved moving from conventional NFIs to Enhanced Forest Inventories (EFIs) [25] that, by integrating NFI plot measurements with remote sensing data, can provide estimates of forest variables, such as growing stock volume (GSV) [23,[26][27][28][29][30][31], annual volume increments [32][33][34], and biomass [29,31], at various spatial scales. Such an approach enables to analyze changes over spatial scales, from national to small scale [23,24], and the information from EFIs can be used for multiple purposes such as to support sustainable management of forest estates by implementing the maps in Forest Information Systems (FIS) and/or in Decision Support System (DSS), besides designing forest management policy strategies.…”