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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, 2025, V. 22, No. 5, pp. 222-233

Assessment and verification of aboveground phytomass using UAV and global datasets: A case study of the southern taiga subzone in Western Siberia

A.O. Eliseev 1 , E.M. Bisirova 1, 2 , I.G. Grachev 1 , I.A. Kerchev 1 
1 Institute of Monitoring of Climatic and Ecological Systems SB RAS, Tomsk, Russia
2 Tomsk Branch of All-Russian Plant Quarantine Center, Tomsk, Russia
Accepted: 27.08.2025
DOI: 10.21046/2070-7401-2025-22-5-222-233
The study presents a comprehensive assessment of aboveground phytomass in forest stands of the southern taiga subzone in Western Siberia (Tomsk Region) by integrating global datasets (GlobBiomass, GEOCARBON, Global Forest Watch (GFW)), field measurements, and aerial photography from a unmanned aerial vehicle (UAV) DJI Phantom 4 Multispectral. An algorithm for extrapolating phytomass using a digital terrain model (DTM) was developed, demonstrating high accuracy (coefficient of determination 0.89) when compared with ground-based data. The study revealed systematic biases in global datasets. The GlobBiomass dataset exhibited the lowest error, with a mean absolute percentage error (MAPE) of 32 %. For the other datasets, errors were significantly higher: GEOCARBON had a MAPE of 43 %, and GFW showed a MAPE of 44 %. These inaccuracies are primarily attributed to coarse spatial resolution and insufficient algorithm adaptation to boreal forests. A key outcome is a high-resolution DTM-based methodology for estimating stand volume, enabling detailed phytomass assessment across a 109-hectare area. The study demonstrates that combining UAV data with local allometric models significantly improves accuracy compared to global maps. The research underscores the importance of validating satellite products and highlights the potential of UAVs for monitoring carbon pools in forest ecosystems.
Keywords: DTM, UAV, GlobBiomass, GEOCARBON, GFW, canopy volume, aboveground phytomass
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