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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. Т. 23. № 3. С. 263-275

Mapping coniferous forest dieback using Earth remote sensing data: A case study of Khamar-Daban ridge forests

M.V. Tsydypova 1 
1 National Research University Higher School of Economics, Moscow, Russia
Accepted: 14.04.2026
DOI: 10.21046/2070-7401-2026-23-3-263-275
The article presents a methodology for mapping coniferous forest decline, based on the integration of automated remote sensing data interpretation techniques, vegetation indices, remote sensing data on above-ground biomass (Biomass CCI (Climate Change Initiative)), and materials from ground-based forest pathology surveys using forests of the Khamar-Daban ridge (Republic of Buryatia) as a case study. The relevance of the research is determined by the spread of dark coniferous forest decline in the study area and the need for effective monitoring of their condition and the dynamics of degradation processes. Multispectral Sentinel-2 satellite imagery was used as source data. A machine learning classification method based on training samples defined from ground data was used for automated detection of decline areas. An assessment of the areas of coniferous forest decline from 2017 to 2024 was conducted in the study area. On the basis of global Biomass CCI data from 2015–2022, an analysis of spatiotemporal trends was performed, revealing a correspondence between areas of biomass decrease and foci of forest decline in the test area. The presented integrated approach enhances the accuracy and informativeness of monitoring coniferous forest decline and can be integrated into forest pathology control systems.
Keywords: coniferous forest decline, remote sensing, automated classification methods, Biomass CCI aboveground biomass data
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