Журнал

About Editorial Board Information for authors and reviewers Publication ethics Contacts User Account: send / review a manuscript
Archive
Volume 23, 2026
Number 1 Number 2
Volume 22, 2025
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 21, 2024
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 20, 2023
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 19, 2022
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 18, 2021
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 17, 2020
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 16, 2019
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 15, 2018
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 14, 2017
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 13, 2016
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 12, 2015
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 11, 2014
Number 1 Number 2 Number 3 Number 4
Volume 10, 2013
Number 1 Number 2 Number 3 Number 4
Volume 9, 2012
Number 1 Number 2 Number 3 Number 4 Number 5
Volume 8, 2011
Number 1 Number 2 Number 3 Number 4
Volume 7, 2010
Number 1 Number 2 Number 3 Number 4
Issue 6, 2009
Volume 1 Volume 2
Issue 5, 2008
Volume 1 Volume 2
Issue 4, 2007
Volume 1 Volume 2
Issue 3, 2006
Volume 1 Volume 2
Issue 2, 2005
Volume 1 Volume 2
Issue 1, 2004
Volume 1
Search
Find:
русский
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. 263-276

Global services for determining forest inventory parameters in forest pathology surveys of declining dark coniferous stands of Perm Krai

L.A. Ivanchina 1 , S.V. Artyushkin 1 , A.Yu. Odintsov 1 
1 Perm State University, Perm, Russia
Accepted: 24.02.2026
DOI: 10.21046/2070-7401-2026-23-2-263-276
In recent years, the need for forest pathology surveys (FPS) has increased significantly in Perm Krai due to the spread of the invasive four-eyed fir bark beetle (Polygraphus proximus Blandford). Widespread dieback of fir stands is currently being observed, making the implementation of remote forest health survey methods increasingly relevant. The purpose of the study is to assess the accuracy of determining forest inventory indicators of drying dark coniferous plantations in Perm Krai using the existing models and online products, as well as the possibility of using them for conducting remote FPS. Currently, there are models and global services for determining forest canopy height and growing stand volume. Practical validation of global canopy height models has revealed significant limitations in their direct use without prior regional calibration. The model used in very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on aerial lidar (VHRM), despite high spatial resolution and detailed display of forests borders, proved unsuitable for quantitative assessment due to a critical systematic underestimation of tree stands heights (maximal bias –9.6 m). Model Global Forest Canopy Height (GFCH) demonstrated the most balanced results by minimizing mean squared error, despite the general tendency to overestimate indicators. A high-resolution canopy height model of the Earth (HRCHM) showed significant temporal instability, which limits its use for retrospective monitoring tasks. Global maps of growing stock volume GlobBiomass Growing Stock Volume (GlobBiomass_GSV), Biomass Climate Change Initiative (Biomass_CCI) demonstrated unsatisfactory results for conditions of Perm Krai. The identified under-estimation of growing stock volume proves these products uninformative for forest management decision making. Consequently, current remote sensing techniques in forest pathological surveys should be viewed as a supplementary tool for preliminary area stratification and ground survey route optimization, rather than a full-scale substitute for instrumental field measurements.
Keywords: forest pathology surveys, remote sensing, stand height, grooving stock volume, Perm Krai, global canopy height maps
Full text

References:

  1. Alekseev A. S., Chernikhovsky D. M., Identification of damage to coniferous stands based on comprehensive analysis of the results of remote sensing of the Earth and ground surveys, Izvestiya vysshikh uchebnykh zavedenii. Lesnoi Zhurnal, 2024, No. 2, pp. 11–28 (in Russian), DOI: 10.37482/0536-1036-2024-2-11-28.
  2. Astapenko S. A., Golubev D. V., Shilkina E. A., Methods and tools of state forest pathology monitoring, Science and Technology of Siberia, 2024, No. 2(13), pp. 47–53 (in Russian).
  3. Bartalev S., Egorov V., Khovratovich T., Krylov A., Stytsenko F., The evaluation of possibilities to assess forest burnt severity using multi-spectral satellite data, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2010, V. 7, No. 3, pp. 215–225 (in Russian).
  4. Bun’kova N. P., Zalesov S. V., Zalesova E. S., Magasumova A. G., Osipenko R. A., Osnovy fitomonitoringa: uchebnoe posobie (Basics of phytomonitoring: Textbook), Ekaterinburg: Ural State Forest Engineering University, 2020, 90 p. (in Russian).
  5. Ivanchina L. A., Shilonosov L. A., Shikhov A. N., Estimation of drying out of dark coniferous forests caused by the spread of the four-eyed fir bark beetle in Perm Krai based on satellite and field observations, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2025, V. 22, No. 3, pp. 149–160 (in Russian), DOI: 10.21046/2070-7401-2025-22-3-149-160.
  6. Krivets S. A., Kerchev I. A., Bisirova E. M. et al., Overview of the current secondary range of the Four-eyed fir bark beetle (Polygraphus proximus Blandford) in the Russian Federation, Russian J. Biological Invasions, 2024, V. 15, No. 2, pp. 180–197, DOI: 10. 1134/S2075111724700061.
  7. Postanovlenie Pravitel’stva RF “Ob utverzhdenii Pravil sanitarnoi bezopasnosti v lesakh” (Resolution of the Government of the Russian Federation “On approval of the Rules for sanitary safety in forests”), Dec. 9, 2020, No. 2047 (in Russian).
  8. Prikaz Ministerstva prirodnykh resursov i ehkologii Rossiiskoi Federatsii “Ob utverzhdenii Lesoustroitel’noi instruktsii” (Order of the Ministry of Natural Resources of the Russian Federation “On approval of the Forest management instructions”), Mar. 29, 2018, No. 122 (in Russian).
  9. Prikaz Ministerstva prirodnykh resursov i ehkologii Rossiiskoi Federatsii “Ob utverzhdenii Poryadka provedeniya lesopatologicheskikh obsledovanii i formy akta lesopatologicheskogo obsledovaniya” (Order of the Ministry of Natural Resources of the Russian Federation “On approval of the Procedure for conducting forest pathology surveys and the form of the forest pathology survey report), Nov. 9, 2020, No. 910 (in Russian).
  10. Chimitdorzhiev T. N., Kirbizhekova I. I., Dmitriev A. V., PolInSAR-based estimation of young pine forest height in winter using TerraSAR-X/TanDEM-X data, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2025, V. 22, No. 4, pp. 101–117 (in Russian), DOI: 10.21046/2070-7401-2025-22-4-101-117.
  11. Astola H., Häme T., Sirro L. et al., Comparison of Sentinel 2 and Landsat 8 imagery for forest variable prediction in boreal region, Remote Sensing of Environment, 2019, V. 223, pp. 257–273, DOI: 10.1016/j. rse.2019.01.019.
  12. Howe A. A., Parks S. A., Harvey B. J. et al., Comparing Sentinel 2 and Landsat 8 for burn severity mapping in western North America, Remote Sensing, 2022, V. 14, Article 5249, DOI: 10.3390/rs14205249.
  13. Hüttich C., Korets M., Bartalev S. et al., Exploiting growing stock volume maps for large scale forest resource assessment: Cross-comparisons of ASAR- and PALSAR-based GSV estimates with forest inventory in Central Siberia, Forests, 2014, V. 5, No. 7, pp. 1753–1776, DOI: 10.3390/f5071753.
  14. Lang N., Jetz W., Schindler K., Wegner J. D., A high-resolution canopy height model of the Earth, Nature Ecology and Evolution, 2023, V. 7(11), pp. 1778–1789, DOI: 10.1038/s41559-023-02206-6.
  15. Lei Y., Wang Y., Wang G. et al., Estimating forest canopy height based on GEDI lidar data and multi-source remote sensing images, The Intern. Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2024, V. XLVIII-1-2024, pp. 297–303, DOI: 10.5194/isprs-archives-XLVIII-1-2024-297-2024.
  16. Lin H., Xu M., Cao C. et al., Estimates of forest canopy height using a combination of ICESat-2/ATLAS data and stereo-photogrammetry, Remote Sensing, 2020, V. 12, Article 3649, DOI: 10.3390/rs12213649.
  17. Santoro M., Cartus O., Carvalhais N. et al., The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations, Earth System Science Data, 2021, V. 13, Iss. 8, pp. 3927–3950, DOI: 10.5194/essd-13-3927-2021.
  18. Schepaschenko D., Moltchanova E., Fedorov S. et al., Russian forest sequesters substantially more carbon than previously reported, Scientific Reports, 2021, V. 11(1), Article 12825, DOI: 10.1038/ s41598-021-92152-9.
  19. Yang L., Liang S., Zhang Y., A new method for generating a global forest aboveground biomass map from multiple high-level satellite products and ancillary information, IEEE J. Selected Topics in Applied Earth Observations and Remote Sensing, 2020, V. 13, pp. 2587–2597, DOI: 10.1109/JSTARS.2020.2987951.
  20. Zhu W., Li Y., Luan K. et al., Forest canopy height retrieval and analysis using random forest model with multi-source remote sensing integration, Sustainability, 2024, V. 16, Article 1735, DOI: 10.3390/ su16051735.