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ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2026, V. 23, No. 2, pp. 216-230

Spatial modeling of aboveground forest biomass from high-resolution satellite data (Kostroma Region case study)

E.N. Sochilova 1 , D.V. Ershov 1 , E.I. Belova 1 , E.A. Gavrilyuk 1 , N.V. Koroleva 1 , S.V. Knyazeva 1 
1 Isaev Centre for Forest Ecology and Productivity RAS, Moscow, Russia
Accepted: 27.01.2026
DOI: 10.21046/2070-7401-2026-23-2-216-230
Assessment and monitoring of forest biomass dynamics is an urgent task for studying forest ecosystem functions and services at different spatial levels. We present our method and results of spatial modeling of aboveground biomass based on key taxation forest characteristics for Kostroma Region. For taxation data mapping, Landsat-8 and -9 multi-season cloudless composite images of 2017–2021 are used. A. Shvidenko’s and D. Schepaschenko’s regression models (published between 2008 and 2023) for calculating the aboveground biomass of stand, undergrowth, and shrubs are applied. Model input data are thematic products of the average stand age, relative stand index, forest site index, and stem wood volume of the dominated tree species of the test region, for which aboveground biomass is calculated in each pixel with a spatial resolution of 30 m. The Random Forest machine-learning algorithm using satellite images performs classification of land cover, dominated tree species, average stand age, relative stand index and forest site index of the region. The volume of stem wood is estimated using the method of nonlinear regression relationships between the spectral reflectance of forest stands in winter cloudless composite Landsat images in the red channel and their taxation characteristics. The classifier is trained using a spatial forest inventory database current as of 2015. The accuracy of the satellite-based forest biomass product is evaluated using an independent set of taxation data plots with known aboveground forest biomass. As a result of comparing two data sets, the mean absolute error (MAE) of biomass by species is in the range of 22.63–27.21 t/ha. The highest mean absolute percentage error (MAPE) is for pine (31.6 %), the lowest is for birch (20.9 %).
Keywords: regional forest mapping, forest characteristics based on remote sensing data, forest biomass reserves, Random Forest, Kostroma Region
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