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Designing a Model of the Picea L. and Abies L. Biomass for Regional Climatic Conditions in Eurasia. P. 38-54

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V.А. Usoltsev, I.S. Tsepordey, I.M. Danilin

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Forest ecosystems play a major role in climate stabilization, and continuous monitoring of their biomass is of paramount importance. Airborne laser scanning technology has become widespread in assessing tree biomass by remote sensing of such inventory indices of trees and stands as crown width and projection area, tree and stand height. The work uses the author’s observation database of 1550 model trees of spruce (Picea L.) and 535 model trees of fir (Abies L.) growing in Eurasia. The database provides information on tree height, crown width and length, as well as biomass of trunk, foliage, branches and roots. It was found that two-factor allometric models of biomass components, including crown width and tree height as independent variables, are the most informative. A positive correlation with crown width and tree height has been identified for all biomass components. Biomass of components of similar-sized fir trees was found to be 45–71 % higher than that of spruce at the statistically reliable level. This is possibly due to the fact that with the same height the crown width of the fir is 11 % less compared to the spruce. The contribution of winter temperatures and precipitation to the variability of biomass components was estimated. The biomass of all components of equal-sized trees is described by a propeller-like 3D dependence. In warm regions, the biomass increases with increasing precipitation; the dependence is characterized by an opposite or neutral trend with the transition to cold regions. The biomass increases with increasing temperature in humid regions, and decreases with the transition to dry climatic conditions. Inclusion of crown length as an additional independent variable in the allometric model practically did not improve its predictive ability. The contribution of inventory indices of trees, their species, and climatic variables to explaining the variability of biomass components is 72.9, 13.5 and 13.6 %, respectively. Climatic variables explain most of the variability in trunk and above-ground biomass (18 to 20 %), least of all in needles and branches (8 to 9 %). The findings can be useful for laser monitoring of forest biomass and predicting possible changes in tree biomass structure in case of climatic deviations.


Vladimir А. Usoltsev1,2*, Doctor of Agriculture, Prof.; ResearcherID: M-8253-2018, ORCID:
Ivan S. Tsepordey2, Candidate of Agriculture, Research Scientist;
ResearcherID: AAC-5377-2020, ORCID:
Igor M. Danilin3, Doctor of Agriculture, Prof.; ResearcherID: AAS-4960-2020, ORCID:


1Botanical Garden of the Ural Branch of the Russian Academy of Sciences, ul. 8 Marta, 202 а, Yekaterinburg, 620144, Russian Federation;*,
2Ural State Forest Engineering University, ul. Sibirskiy trakt, 37, Yekaterinburg, 620100, Russian Federation;,
3V.N. Sukachev Institute of Forest SB RAS, Akademgorodok, 50, Krasnoyarsk, 660036, Russian Federation;


hydrothermal gradients, biomass components, LiDAR sensing of trees, allometric models, mean January temperature, annual precipitation, spruce, fir


The research was carried out within the framework of the state assignment FEUZ-2021-0014

For citation

Usoltsev V.А., Tsepordey I.S., Danilin I.M. Designing a Model of the Picea L. and Abies L. Biomass for Regional Climatic Conditions in Eurasia. Lesnoy Zhurnal = Russian Forestry Journal, 2022, no. 6, pp. 38–54. (In Russ.).


  1. Belov S.V., Artsybashev E.S. Studying the Reflectivity of Wood Species. Botanicheskii Zhurnal, 1957, vol. 42, no. 4, pp. 517–534. (In Russ.).

  2. Belov M.L., Fesenko Yu.S., Gorodnichev V.A., Kuvshinov A.V. Laser Remote Sensing of Vegetation Cover Using Reflection Coefficients Measurement Data. Radiooptics of the Bauman MSTU, 2016, no. 3, pp. 1–17. (In Russ.).

  3. Veyisov S., Kaplin V.G. To the Method of Biomass Estimating in White Saxaul of the Eastern Kara-Kum Desert. Problems of Desert Development, 1976, no. 1, pp. 60–64.(In Russ.).

  4. Danilin I.M., Medvedev Е.М., Melnikov S.R. Laser Scanning of the Earth and Forests. Krasnoyarsk, V.N. Sukachev Institute of Forest SB RAS Publ., 2005. 182 p. (In Russ.).

  5. Pozdnyakov L.K., Protopopov V.V., Gorbatenko V.M. Biological Productivity of Forests in Middle Siberia and Yakutia. Krasnoyarsk, Knizhnoye Izdatel’stvo, 1969. 155 p. (In Russ.).

  6. Samoylovich G.G. The Use of Aerial Photography and Aviation in Forestry. Moscow, Goslesbumizdat Publ., 1953. 476 p. (In Russ.).

  7. Usoltsev V.A. Growth and Structure of the Phytomass of Forest Stands. Novosibirsk, Nauka Publ., 1988. 253 p. (In Russ.).

  8. Usoltsev V.A. Compiling Forest Biomass Data Banks. Yekaterinburg, UB RAS Publ., 1998. 541 p. (In Russ.).

  9. Usoltsev V.A., Vorobeichik E.L., Bergman I.E. Biological Productivity of Ural Forests under Conditions of Air Pollutions: An Investigation of a System of Regularities. Yekaterinburg, USFEU Publ., 2012. 365 p. (In Russ.).

  10. Usoltsev V.A., Usoltsevа R.F. Results of Approximating Birch and Aspen Aboveground Biomass on the Diameter and Height of a Tree Stem. Vestnik sel’sko-khozyaystvennoy nauki Kazakhstana, 1977, iss. 7, pp. 83–89. (In Russ.).

  11. Usoltsev V.A., Chasovskikh V.P., Noritsin D.V. Age Dynamics and Structure of Spruce and Fir Tree Biomass in the Forests of Eurasia. Eko-potentsial, 2015, no. 4(12), pp. 11–13. (In Russ.).

  12. Baskerville G.L. Use of Logarithmic Regression in the Estimation of Plant Biomass. Canadian Journal of Forest Research, 1972, vol. 2, no. 1, pp. 49–53.

  13. Blanchette D., Fournier R.A., Luther J.E., Côté J.-F. Predicting Wood Fiber Attributes Using Local-Scale Metrics from Terrestrial LiDAR Data: A Case Study of Newfoundland Conifer Species. Forest Ecology and Management, 2015, vol. 347, pp. 116–129.

  14. Burger H. Der Kronenaufbau gleichalteriger Nadelholzbestände. Mitteilungen der Schweizerischen Anstalt für das forstliche Versuchswesen, 1939, vol. 21, iss. 1, pp. 5–57. (In Ger.).

  15. Cannell M.G.R. Woody Biomass of Forest Stands. Forest Ecology and Management, 1984, vol. 8, iss. 3-4, pp. 299–312.

  16. Chave J., Réjou-Méchain M., Búrquez A., Chidumayo E., Colgan M.S., Delitti W.B.C. et al. Improved Allometric Models to Estimate the Aboveground Biomass of Tropical Trees. Global Change Biology, 2014, vol. 20, iss. 10, pp. 3177–3190.

  17. Fu L., Liu Q., Sun H., Wang Q., Li Z., Chen E., Pang Y., Song X., Wang G. Development of a System of Compatible Individual Tree Diameter and Aboveground Biomass Prediction Models Using Error-in-Variable Regression and Airborne LiDAR Data. Remote Sensing, 2018, vol. 10, iss. 2, art. 325.

  18. Fu W., Wu Y. Estimation of Aboveground Biomass of Different Mangrove Trees Based on Canopy Diameter and Tree Height. Procedia Environmental Sciences, 2011, vol. 10, part C, pp. 2189–2194.

  19. Goodman R.C., Phillips O.L., Baker T.R. The Importance of Crown Dimensions to Improve Tropical Tree Biomass Estimates. Ecological Applications, 2014, vol. 24, iss. 4, pp. 680–698.

  20. Hancock S., Anderson K., Disney M., Gaston K.J. Measurement of Fine-Spatial-Resolution 3D Vegetation Structure with Airborne Waveform Lidar: Calibration and Validation with Voxelised Terrestrial Lidar. Remote Sensing of Environment, 2017, vol. 188, pp. 37–50.

  21. Jucker T., Caspersen J., Chave J., Antin C., Barbier N., Bongers F. et al. Allometric Equations for Integrating Remote Sensing Imagery into Forest Monitoring Programmes. Global Change Biology, 2017, vol. 23, iss. 1, pp. 177–190.

  22. Kindermann G.E., McCallum I., Fritz S., Obersteiner M. A Global Forest Growing Stock, Biomass and Carbon Map Based on FAO Statistics. Silva Fennica, 2008, vol. 42, no. 3, pp. 387–396.

  23. Li J., Hu B., Noland T.L. Classification of Tree Species Based on Structural Features Derived from High Density LiDAR Data. Agricultural and Forest Meteorology, 2013, vol. 171-172, pp. 104–114.

  24. Machimura T., Fujimoto A., Hayashi K., Takagi H., Sugita S. A Novel Tree Biomass Estimation Model Applying the Pipe Model Theory and Adaptable to UAV-Derived Canopy Height Models. Forests, 2021, vol. 12, iss. 2, art. 258.

  25. Neuville R., Bates J.S., Jonard F. Estimating Forest Structure from UAV-Mounted LiDAR Point Cloud Using Machine Learning. Remote Sensing, 2021, vol. 13, iss. 3, art. 352.

  26. Poorter H., Jagodzinski A.M., Ruiz-Peinado R., Kuyah S., Luo Y., Oleksyn J., Usoltsev V.A., Buckley T.N., Reich P.B., Sack L. How Does Biomass Allocation Change with Size and Differ among Species? An Analysis for 1200 Plant Species from Five Continents. New Phytologist, 2015, vol. 208, iss. 3, pp. 736–749.

  27. Ubuy M.H., Eid T., Bollandsås O.M., Birhane E. Aboveground Biomass Models for Trees and Shrubs of Exclosures in the Drylands of Tigray, Northern Ethiopia. Journal of Arid Environments, 2018, vol. 156, pp. 9–18.

  28. Usoltsev V.A. Single-Tree Biomass Data for Remote Sensing and Ground Measuring of Eurasian Forests. Yekaterinburg, USFEU Publ., 2020.

  29. Usoltsev V.A., Shobairi S.O.R., Chasovskikh V.P. Triple Harmonization of Transcontinental Allometric of Picea spp. and Abies spp. Forest Stand Biomass. Ecology, Environment and Conservation, 2018, vol. 24, no. 4, pp. 1966–1972. Available at: (accessed 23.04.21)

  30. Usoltsev V.A., Shobairi S.O.R., Tsepordey I.S., Ahrari A., Zhang M., Shoaib A.A., Chasovskikh V.P. Are There Differences in the Response of Natural Stand and Plantation Biomass to Changes in Temperature and Precipitation? A Case for Two-Needled Pines in Eurasia. Journal of Resources and Ecology, 2020, vol. 11, no. 4, pp. 331–341.

  31. Vanninen P., Ylitalo H., Sievänen R., Mäkelä A. Effects of Age and Site Quality on the Distribution of Biomass in Scots Pine (Pinus sylvestris L.). Trees, 1996, vol. 10, iss. 4, pp. 231–238.

  32. West G.B., Brown J.H., Enquist B.J. A General Model for the Origin of Allometric Scaling Laws in Biology. Science, 1997, vol. 276, iss. 5309, pp. 122–126.

  33. World Weather Map. Maps of World. Available at: (accessed 23.04.21)

  34. Zeng W.S., Duo H.R., Lei X.D., Chen X.Y., Wang X.J., Pu Y., Zou W.T. Individual Tree Biomass Equations and Growth Models Sensitive to Climate Variables for Larix spp. in China. European Journal of Forest Research, 2017, vol. 136, pp. 233–249.


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