Crown’s and canopy’s morphometry-based estimation models for the tree’s and stand’s trunk diameter of forest-forming species of northern Eurasia available for lidar scanning

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Abstract

Within the framework of climate-smart forestry, accurate information on phytomass and carbon deposition capacity of forests is necessary. To date, many empirical models have been published and many taxation standards have been compiled to assess the trees’ and stands’ phytomass based on morphometric indicators measured in sample plots. However, using them in order to assess the carbon-depositing capacity of forests across large areas by means of traditional land-based forest inventory is quite a laborious undertaking. The laser (lidar) technology may be used as an alternative, but it does not allow determining the main mass-forming parameters — the tree trunk’s diameters or the average trunk diameter within the stand. To combine traditional empirical models and tables of phytomass with remote sensing data, intermediate models are needed to estimate the diameter of the tree trunk or the average trunk diameter within the stand, depending on the morphometry of the canopy, recorded either by terrestrial methods or remotely. The purpose of this study was to design the models of the tree trunk’s diameter’s dependence and the average trunk diameter within the stand on the main morphometric indicators of the canopy, obtained by ground measurements, but also available for lidar scanning. The models are constructed at the level of genera as aggregates of vicarious species. The materials of two previously compiled databases on phytomass and morphometric structure of 5320 trees and 5817 stands of Eurasia were used as the initial data for the study. Two-factor allometric dependences were constructed for 13 genera: (a) the stem diameter at breast height relation to the height of the tree and the diameter of the crown and (b) the average trunk diameter of the stand relation to the average tree height and stand’s density, explaining in most cases from 90 to 97% of the dependent variable’s variability. The proposed models based on traditional ground-based taxation data can be directly applied in lidar technologies or used to validate models based on lidar sensing data. This is especially important due to the lack of ground-based measurements of tree and stand morphometry for most existing species and habitats. The use of the proposed models based on the results of remote registration of crown and canopy morphology makes it possible to assess the phytomass and carbon pool of trees and stands in some territories in real time by combining them with available standards and specifications for determining the phytomass of trees and stands.

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V. А. Usoltsev

Ural State Forest Engineering University; Ural State University of Economics

Author for correspondence.
Email: Usoltsev50@mail.ru
Russian Federation, Sibirskiy Trakt, 37, Yekaterinburg, 620100; 8 Marta str./Narodnaya Volya, 62/45, Yekaterinburg, 620144

References

  1. Altyntsev M.A., Saber K. Kh.M., Metodika avtomatizirovannoi fil’tratsii dannykh mobil’nogo lazernogo skanirovaniya (The technique of automated filtering of mobile laser scanning data), Vestnik Sibirskogo gosudarstvennogo universiteta geosystem i tekhnologii, 2021, No. 3, pp. 5—19.
  2. Antanaitis V.V., Vvedenie (Introduction). In: Zakonomernosti lesnoi taksatsii: Metodicheskoe posobie (Patterns of forest taxation: A methodological guide), Kaunas: Lithuanian Agricultural Academy, 1976, pp. 5—10.
  3. Antanaitis V.V., Tyabera A.P., Shyapetene Ya.A., Zakony, zakonomernosti rosta i stroeniya drevostoev: Metodicheskoe posobie (Laws, patterns of growth and structure of stands: A methodological guide), Kaunas: Lithuanian Agricultural Academy, 1986. 157 p.
  4. Assman E., Die Bedeutung des “erweiterten Eichhorn’schen Gesetzes” für die Kontrolle von Fichten Ertragstafeln, Forstwissenschaftliches Centralblatt, 1955, Vol. 74, pp. 321—330.
  5. Assmann E., Waldertragskunde: Organische Produktion, Struktur, Zuwachs und Ertrag von Waldbeständen, München, Bonn, Wien: BLV Verlagsgesellschaft, 1961, 492 p.
  6. Baskerville G.L., Use of logarithmic regression in the estimation of plant biomass, Canadian Journal of Forest Research, 1972, Vol. 2, pp. 49—53.
  7. Besnard S., Koirala S., Santoro M., Weber U., Nelson J., Gütter J., Herault B., Kassi J., N’Guessan A., Neigh C., Poulter B., Zhang T., Carvalhais N., Mapping global forest age from forest inventories, biomass and climate data, Earth System Science Data, 2021, Vol. 13, pp. 4881—4896.
  8. Bi H., Fox J.C., Li Y., Lei Y., Pang Y., Evaluation of nonlinear equations for predicting diameter from tree height, Canadian Journal of Forest Research, 2012, Vol. 42, pp. 789—806.
  9. Boyko E.S., Karagyan A.V., Tsifrovoe modelirovanie drevesno-kustarnikovoi rastitel’nosti akkumulyativnykh beregov po dannym vozdushnogo lazernogo skanirovaniya (Digital modeling of tree and shrub vegetation of accumulative shores based on aerial laser scanning data), Vestnik Sibirskogo gosudarstvennogo universiteta geosystem i tekhnologii, 2021, No. 2, pp. 103—114.
  10. Brandeis T., Randolph K.C., Strub M.R., Modelling Caribbean tree stem diameters from tree height and crown width measurements, Mathematical and Computational Forestry & Natural-Resource Sciences, 2009, Vol. 1, No. 2, pp. 78—85.
  11. Cao Q.V., Dean T.J., Predicting diameter at breast height from total height and crown length, Proceedings of the 15th biennial southern silvicultural research conference, Asheville, NC: U.S.D.A, Forest Service, Southern Research Station. Tech. Rep. SRS-GTR-175, 2013, pp. 201—205.
  12. Chang A. Jung J., Kim Y., Estimation of forest stand diameter class using airborne lidar and field data, Remote Sensing Letters, 2015, Vol. 6, No. 6, pp. 419—428.
  13. Chetyrkin E.M., Statisticheskie metody prognozirovaniya (Statistical forecasting methods), Moscow: Statistika, 1977, 200 p.
  14. Coops N.C., Tompalski P., Goodbody T.R.H., Queinnec M., Luther J.E., Bolton D.K., White J.C., Wulder M.A., van Lier O.R., Hermosilla T., Modelling LiDAR-derived estimates of forest attributes over space and time: a review of approaches and future trends, Remote Sensing, 2021, Vol. 260, Article 112477.
  15. Danilin I.M., Medvedev E.M., Melnikov S.R., Lazernaya lokatsiya Zemli i lesa (Laser location of the Earth and forests: A textbook), Krasnoyarsk: Institute of Forests SB RAS, 2005, 182 p.
  16. Dean T.J., Cao Q.V., Roberts S.D., Evans D.L., Measuring heights to crown base and crown median with LiDAR in a mature, even-aged loblolly pine stand, Forest Ecology and Management, 2009, Vol. 257, pp. 126—133.
  17. Dean T.J., Long J.N. Validity of constant-stress and elastic-instability principles of stem formation in Pinus contorta and Trifolium pretense, Annals of Botany, 1986, Vol. 58, pp. 833—840.
  18. Demidov V.E., Primenenie vozdushnogo lazernogo skanirovaniya dlya kartirovaniya rel’efa, poiska sledov antropogennogo vozdeistviya i izucheniya rastitel’nogo pokrova na territorii Prioksko-terrasnogo gosudarstvennogo prirodnogo biosfernogo zapovednika (Application of aerial laser scanning for terrain mapping, search for traces of anthropogenic impact and study of vegetation cover on the territory of the Prioksko-Terrasny State Natural Biosphere Reserve), Trudy Mordovskogo gosudarstvennogo prirodnogo zapovednika im. P.G. Smidovicha, 2021, No. 28, pp. 74—82.
  19. Drake J.B., Dubayah R.O., Knox R.G., Clark D.B., Blair J.B., Sensitivity of large-footprint lidar to canopy structure and biomass in a neotropical rainforest, Remote Sensing of Environment, 2002, Vol. 81, pp. 378—392.
  20. Eichhorn F., Ertragstafeln für die Weißtanne. Berlin: Springer, 1902, 118 p.
  21. Eichhorn F., Beziehungen zwishen Bestandeshöhe und Bestandesmasse, Allgemeine Forst- und Jagdzeitung, 1904, Vol. 80, pp. 45—49.
  22. Filipescu C.N., Groot A., MacIsaac D.A., Cruickshank M.G., Stewart J.D., Prediction of diameter using height and crown attributes: a case study, Western Journal of Applied Forestry, 2012, Vol. 27, No. 1, pp. 30—35.
  23. Fu L., Duan G., Ye Q., Meng X., Luo P., Sharma R.P., Sun H., Wang G., Liu Q., Prediction of individual tree diameter using a nonlinear mixed-effects modeling approach and airborne LiDAR data, Remote Sensing, 2020, Vol. 12, Article 1066.
  24. Galvincio J.D., Popescu S.C., Measuring individual tree height and crown diameter for mangrove trees with airborne lidar data, International Journal of Advanced Engineering, Management and Science, 2016, Vol. 2, No. 5, pp. 431—443.
  25. Gavrikov V.L., A simple theory to link bole surface area, stem density and average tree dimensions in a forest stand, European Journal of Forest Research, 2014, Vol. 133, No. 6, pp. 1087—1094.
  26. Gerhardt E., Über Bestandes- Wachstumsgesetze und ihre Anwendung zur Aufstellung von Ertragstafeln, Allgemeine Forst- und Jagdzeitung, 1909, Vol. 85, pp. 117—128.
  27. Gerhardt E., Zur Ertragstafelfrage: Eine dreiteilige Fichtenertragstafel, Allgemeine Forst- und Jagdzeitung, 1928, Vol. 104, pp. 377—386.
  28. Global Mapper: Getting Started Guide. Blue Marble Geographics, 2018, 24 p., available at: https://www.bluemarblegeo.com/docs/guides/global-mapper-19-gettingstarted-guide-en.pdf (October 13, 2019).
  29. Gonzalez-Benecke C.A., Fernández M.P., Gayoso J., Pincheira M., Wightman G., Using tree height, crown area and stand-level parameters to estimate tree diameter, volume, and biomass of Pinus radiata, Еucalyptus globulus and Еucalyptus nitens, Forests, 2022, Vol. 13, No. 12, Article 2043.
  30. Gonzalez-Benecke C.A., Gezan S.A., Samuelson L.J., Cropper W.P., Leduc D.J., Martin T.A., Estimating Pinus palustris tree diameter and stem volume from tree height, crown area and stand-level parameters, Journal of Forestry Research, 2014, Vol. 25, pp. 43—52.
  31. Gould S., Allometry and size in ontogeny and phylogeny, Biological Reviews, 1966, Vol. 41, pp. 587—640.
  32. Hao Y., Widagdo F.R.A., Liu X., Quan Y., Dong L., Li F., Individual tree diameter estimation in small-scale forest inventory using UAV laser scanning, Remote Sensing, 2021, Vol. 13, No. 13, Article 24.
  33. Harding D.J., Lefsky M.A., Parker G.G., Blair J.B., Laser altimeter canopy height profiles: methods and validation for closed-canopy, broadleaf forests, Remote Sensing of Environment, 2001, Vol. 76, pp. 283—297.
  34. Harrington R., Comparison of field- and LIDAR-derived tree crown parameters in mid-rotation loblolly pine. M. Sc. thesis, Mississippi State University, Mississippi State, MS, 2001, 43 p.
  35. Inoue A., Koyama R., Koshikawa K., Yamamoto K., Comparison of models for estimating stem surface area of coniferous trees grown in old-growth natural forests, Journal of Forestry Research, 2021, Vol. 26, No. 1, pp. 1—6.
  36. Inoue A., Nishizono T. Conservation rule of stem surface area: a hypothesis, European Journal of Forest Research, 2015, Vol. 134, No. 4, pp. 599—608.
  37. Ivanova N.V., Shashkov M.P., Shanin V.N., Opredelenie kharakteristik smeshannykh drevostoev po dannym aerofotos’emki s primeneniem bespilotnogo letatel’nogo apparata (BPLA)) (Determination of characteristics of mixed stands according to aerial photography using an unmanned aerial vehicle (UAV)), Vestnik Tomskogo gosudarstvennogo universiteta. Biologiya, 2021, No. 54, pp. 158—175.
  38. Jerez Rico M., Modeling canopy structure effects on loblolly pine growth, 2002, LSU Doctoral Dissertations, 837, 79 р., available at: https://repository.lsu.edu/gradschool_dissertations/837.
  39. Kabonen A.V., Ivanova N.V., Otsenka biometricheskikh kharakteristik dereviev po dannym nazemnogo lidar i raznosezonnoj aerofotos’emki v iskusstvennykh nasazhdeniyakh (Assessment of biometric characteristics of trees according to ground lidar and multi-season aerial photography in artificial forest stands), Nature Conservation Research. Zapovednaya Nauka, 2023, No. 1, pp. 64—83.
  40. Kalliovirta J., Tokola T., Functions for estimating stem diameter and tree age using tree height, crown width and existing stand database information, Silva Fennica, 2005, Vol. 39, pp. 227—248.
  41. Kovyazin V.F., Vinogradov K.P., Kitsenko A.A., Vasilyeva E.A., Vozdushnoe lazernoe skanirovanie dlya utochneniya taksatsionnykh kharakteristik drevostoev (Aerial laser scanning to clarify the taxation characteristics of stands), Izvestiya vuzov. Lesnoi zhurnal, 2020, No. 6, pp. 42—54.
  42. Lee D., Choi J., Evaluating maximum stand density and size–density relationships based on the competition density rule in Korean pines and Japanese larch, Forest Ecology and Management, 2019, Vol. 446, pp. 204—213.
  43. Lee S.H., Kim D.H., Jeong J.H., Han S.H., Kim S., Park H.J., Kim H.J., Developing a yield table and analyzing the economic feasibility for Acacia hybrid plantations in achieving carbon neutrality in southern Vietnam, Forests, 2022, Vol. 13, No. 8, Article 1316.
  44. Lefsky M.A., Cohen W.B., Acker S.A., Parker G.G., Spies T.A., Harding D., LIDAR remote sensing of the canopy structure and biophysical properties of Douglas-fir western hemlock forests, Remote Sensing of Environment, 1999, Vol. 70, pp. 339—361.
  45. Leskinen P., Lindner M., Verkerk P.-Y., Nabours G.-Ya., van Brusselen J., Kulikova E., Hassegawa M., Lerink B. (eds.), Lesa Rossii i izmenenie klimata. Chto nam mozhet skazat’ nauka? (Forests of Russia and climate change. What can science tell us?), 11, European Forest Institute, 2020, 140 p. https://doi.org/10.36333/wsctu11
  46. Li W., Duveiller G., Wieneke S., Forkel M., Gentine P., Reichstein M., Niu S., Migliavacca M., Orth R., Regulation of the global carbon and water cycles through vegetation structural and physiological dynamics, Environmental Research Letters, 2024, Vol. 19, Article 073008.
  47. Liepa I.Y., Dinamika drevesnykh zapasov: prognozirovanie i ekologiya (Dynamics of wood stocks: forecasting and ecology), Riga: Zinatne, 1980, 170 p.
  48. Luo Y., Wang X., Ouyang Z., Lu F., Feng L., Tao J., A review of biomass equations for China’s tree species, Earth System Science Data, 2020, Vol. 12, No. 1, pp. 21—40.
  49. Luoma V., Saarinen N., Wulder M.A., White J.C., Vastaranta M., Holopainen M., Hyyppä J., Assessing precision in conventional field measurements of individual tree attributes, Forests, 2017, Vol. 8, No. 2, Article 38.
  50. Magin R., Möglichkeiten der dynamischen Bonitierung im Hinblick auf die künftige Einheitsbewertung, Allgemeine Forst Zeitschrift, 1955, Vol. 10, pp. 122—124.
  51. Magnussen S., Boudewyn P., Derivations of stand heights from airborne laser scanner data with canopy-based quantile estimators, Canadian Journal of Forest Research, 1998, Vol. 28, pp. 1016—1031.
  52. Maltamo M., Mustonen K., Hyyppä J., Pitkänen J., Yu X., The accuracy of estimating individual tree variables with airborne laser scanning in a boreal nature reserve, Canadian Journal of Forest Research, 2004, Vol. 34, pp. 1791—1801.
  53. McLone R.R., Matematicheskoe modelirovanie — iskusstvo primeneniya matematiki (Mathematical modeling — the art of applying mathematics), In: Matematicheskoe modelirovanie (Mathematical modeling), Moscow: Mir, 1979, pp. 9—20.
  54. Means J.E., Acker S.A., Fitt J.B., Renslow M., Emerson L., Hendrix C.J., Predicting forest stand characteristics with airborne scanning lidar, Photogrammetric Engineering and Remote Sensing, 2000, Vol. 66, pp. 1367—1371.
  55. Metzger K. Der Wind als massgebender Faktor für das Wachsthum der Bäume, Mündener forstliche Hefte, 1893, Vol. 3, pp. 35—86.
  56. Moriguchi K., Ueki T., Saito M., Responses of spacing indices for relative yield based on the reciprocal competition-density effect, Forest Science, 2017, Vol. 63, No. 5, pp. 485—495.
  57. Moshkalev A.G., Davidov G.M., Yanovskiy L.N., Moiseyev V.S., Stolyarov D.P., Burnevskiy Yu.I., Lesotaksatsionnyj spravochnik po Severo-Zapadu SSSR (Forest Inventory Handbook for the North-West of the USSR), Leningrad: LLTA, 1984, 319 p.
  58. Nalimov V.V., Teoriya eksperimenta (Theory of experiment), Moscow: Nauka, 1971, 208 p.
  59. Newton P.F., Stand density management diagrams: modelling approaches, variants, and exemplification of their potential utility in crop planning, Canadian Journal of Forest Research, 2021, Vol. 51, No. 2, pp. 236—256.
  60. Özdemir İ., Estimation of forest stand parameters using airborne LIDAR data, SDU Faculty of Forestry Journal, 2013, Vol. 14, pp. 31—39.
  61. Panagiotidis D., Abdollahnejad A., Surový P., Chiteculo V., Determining tree height and crown diameter from high-resolution UAV imagery, International Journal of Remote Sensing, 2017, Vol. 38, No. 8—10, pp. 2392—2410.
  62. Parker R.C., Evans D.L., An application of LiDAR in a double-sample forest inventory, Western Journal of Applied Forestry, 2004, Vol. 19, pp. 95—101.
  63. Parker R.C., Mitchel A.L., Smoothed versus unsmoothed LiDAR in a double-sample forest inventory, Southern Journal of Applied Forestry, 2005, Vol. 29, pp. 40—47.
  64. Pereira Martins-Neto R., Garcia Tommaselli A.M., Imai N.N., Honkavaara E., Miltiadou M., Saito Moriya E.A., David H.C., Tree species classification in a complex Brazilian tropical forest using hyperspectral and LiDAR data, Forests, 2023, Vol. 14, Article 945. https://doi.org/10.3390/f14050945
  65. 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, No. 3, pp. 736—749.
  66. Popescu S.C., Estimating biomass of individual pine trees using airborne lidar, Biomass and Bioenergy, 2007, Vol. 31, pp. 646—655.
  67. Salas C., Ene L., Gregoire T.G., Næsset E., Gobakken T., Modelling tree diameter from airborne laser scanning derived variables: A comparison of spatial statistical models, Remote Sensing of Environment, 2010, Vol. 114, pp. 1277—1285.
  68. Sexton J.O., Bax T., Siqueira P., Swenson J.J., Hensley S., A comparison of lidar, radar, and field measurements of canopy height in pine and hardwood forests of southeastern North America, Forest Ecology and Management, 2009, Vol. 257, pp. 1136—1147.
  69. Sharma R.P., Bílek L., Vacek Z., Vacek S., Modelling crown width–diameter relationship for Scots pine in the Central Europe, Trees — Structure and Function, 2017, Vol. 31, pp. 1875—1889.
  70. Smolina A., Illarionova S., Shadrin D., Kedrov A., Burnaev E. Forest age estimation in northern Arkhangelsk region based on machine learning pipeline on Sentinel-2 and auxiliary data, Scientific Reports, 2023, Vol. 13, Article 22167.
  71. Stereńczak K., Mielcarek M., Wertz B., Bronisz K., Zajączkowski G., Jagodziński A.M., Ochał W., Skorupski M., Factors influencing the accuracy of ground-based tree-height measurements for major European tree species, Journal of Environmental Management, 2019, Vol. 231, pp. 1284—1292.
  72. Sun Y., Jin X., Pukkala T., Li F., Predicting individual tree diameter of larch (Larix olgensis) from UAV—LiDAR data using six different algorithms, Remote Sensing, 2022, Vol. 14, Article 1125.
  73. Ter-Mikaelian M.T., Korzukhin M.D., Biomass equations for sixty-five North American tree species, Forest Ecology and Management, 1997, Vol. 97, pp. 1—24.
  74. Thomasius H., Untersuchungen über die Brauchbarkeit einiger Wachstumsgrößen von Bäumen und Beständen für die quantitative Standortsbeurteilung, Archiv für Forstwesen, 1963, Vol. 12, No. 12, pp. 1267—1323.
  75. Tsepordey I.S., Biologicheskaya produktivnost’ lesoobrazuyushchikh vidov v klimaticheskom kontekste Evrazii (Biological productivity of forest-forming species in the climatic context of Eurasia) (edited by prof. V.A. Usoltsev), Yekaterinburg: Izdatel’stvo UMTS UPI, 2023, 467 p., available at: https://elar.usfeu.ru/handle/123456789/12450
  76. Umemi K., Inoue A., A model for predicting mean diameter at breast height from mean tree height and stand density, Journal of Forest Research, 2024, Vol. 29, No. 3, pp. 186—195.
  77. Usoltsev V.A., Biologicheskaya produktivnost’ lesoobrazuyushchikh porod v klimaticheskikh gradientakh Evrazii: k menedzhmentu biosfernykh funktsij lesov (Biological productivity of forest-forming species in the climatic gradients of Eurasia: on the management of biospheric functions of forests), Yekaterinburg: Ural State Forestry Engineering University, 2016a. 384 p., available at: http://elar.usfeu.ru/handle/123456789/5634.
  78. Usoltsev V.A., Biologicheskaya produktivnost’ lesov Severnoj Evrazii: Metody, baza dannykh i ee prilozheniya (Biological productivity of forests of Northern Eurasia: Methods, database and its applications), Yekaterinburg: Ural Branch of the Russian Academy of Sciences, 2007, 636 p., available at: http://elar.usfeu.ru/handle/123456789/3281.
  79. Usoltsev V.A., Fitomassa i pervichnaya produktsiya lesov Evrazii (Phytomass and primary production of Eurasian forests), Yekaterinburg: Ural Branch of the Russian Academy of Sciences, 2010, 570 p., available at: http://elar.usfeu.ru/handle/123456789/2606.
  80. Usoltsev V.A., Fitomassa lesov Severnoj Evrazii: baza dannykh i geografiya (Phytomass of forests of Northern Eurasia: database and geography), Yekaterinburg: Publishing House of the Ural Branch of the Russian Academy of Sciences, 2001, 708 p., available at: http://elar.usfeu.ru/handle/123456789/3280.
  81. Usoltsev V.A., Fitomassa lesov Severnoj Evrazii: Normativy i elementy geografii (Phytomass of forests of Northern Eurasia: Standards and elements of geography), Yekaterinburg: Publishing House of the Ural Branch of the Russian Academy of Sciences, 2002, 762 p., available at: http://elar.usfeu.ru/handle/123456789/3302.
  82. Usoltsev V.A., Fitomassa model’nykh dereviev dlya distantsionnoj i nazemnoj taksatsii lesov Evrazii. Elektronnaya baza dannykh. 3-e dopolnennoe izdanie. (Phytomass of model trees for remote and terrestrial forest taxation in Eurasia. An electronic database. 3rd expanded edition), Yekaterinburg: Botanical Garden of the Ural Branch of the Russian Academy of Sciences, Ural State Forestry University, 2023a, 1 electron. opt. disk (CD-ROM), available at: https://elar.usfeu.ru/handle/123456789/12451.
  83. Usoltsev V.A., Biomass and primary production of Eurasian forests. An electronic database. 4th expanded edition. Yekaterinburg: Botanical Garden of the Ural Branch of the Russian Academy of Sciences, Ural State Forestry University, 2023b, 1 electron. opt. disk (CD-ROM)., available at: https://elar.usfeu.ru/handle/123456789/12452
  84. Usoltsev V.А., Modeli dlya otsenki vozrasta fereviev i drevostoev lesoobrazuyushchikh vidov Evrazii po mohometrii kroon i pologa, dostupnoj dlya vozdushnogo lazernogo skanirovaniya (Models for estimating the age of trees and stands of forest-forming species of Eurasia based on crown and canopy morphometry available for aerial laser scanning), Biosphеra, 2024, No. 4, pp. 399—406.
  85. Usoltsev V.A., Perspektivy 3D-modelirovaniya prostranstvennoj struktury fitomassy lesov (Prospects for 3D modeling of the spatial structure of forest phytomass), Eco-potentsial, 2014, No. 2, pp. 55—71., available at: https://elar.usfeu.ru/bitstream/123456789/3356/1/Usoltsev.pdf
  86. Usoltsev V.A., Phytomass of model trees of forest-forming species of Eurasia: database, climatically determined geography, taxation standards. Yekaterinburg: Ural State Forest Engineering Univ., 2016., 336 p., available at: http://elar.usfeu.ru/handle/123456789/5696
  87. Usoltsev V.A., Tsepordey I.S., Chasovskikh V.P., Modeli dlya otsenki biomassy dereviev lesoobrazuyushchikh vidov po diametry krony v svyazi s ispol’zovaniem dronov (Models for estimating the biomass of trees of forest-forming species by crown diameter in relation to the use of drones), Khvoinye boreal’noi zony, 2023, Vol. 41, No. 4, pp. 300—305.
  88. Usoltsev V.A., Tsepordei I.S., Vozrastnye izmeneniya v structure nadzemnoi fitomassy lesoobrazuyushchikh vidov Evrazii (Age-related changes in the structure of aboveground phytomass of forest-forming species of Eurasia), Lesovedenie, 2023, No. 6, pp. 563—576.
  89. Usoltsev V.A., Tsepordey I.S., Plyukha N.I. Vzaimosvyazi diametrov stvola i krony lesoobrazuyushchikh vidov (Interrelations of stem and crown diameters of forest-forming species of Eurasia, Izvestia Sankt-Peterburgskoi Lesotehniceskoi Akademii, 2024, Issue 250, pp. 176—199. doi: 10.21266/2079-4304.2024.250.176-199.
  90. 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, pp. 122—126.
  91. West G.B., Brown J.H., Enquist B.J., A general model for the structure and allometry of plant vascular system, Nature, 1999, Vol. 400, pp. 664—667.
  92. Whitfield J., All creatures great and small, Nature, 2001, Vol. 413, pp. 342—344.
  93. Wirth C., Schumacher J., Schulze E.-D., Generic biomass functions for Norway spruce in Central Europe — a meta-analysis approach toward prediction and uncertainty estimation, Tree Physiology, 2004, Vol. 24, pp. 121—139.
  94. Xu Q., Li B., Maltamo M., Tokola T., Hou Z., Predicting tree diameter using allometry described by non-parametric locally-estimated copulas from tree dimensions derived from airborne laser scanning, Forest Ecology and Management, 2019, Vol. 434, pp. 205—212.
  95. Yao W., Krull J., Krzystek P., Heurich M., Sensitivity analysis of 3D individual tree detection from LiDAR point clouds of temperate forests, Forests, 2014, Vol. 5, pp. 1122—1142.
  96. Young B., Comparison of field and LiDAR measurements of loblolly pine, M. Sc. thesis, Mississippi State University, Mississippi State, MS, 2000, 76 p.
  97. Yunson E.V., Melnichuk D.Y., Tsifrovoi dvoinik lesnogo massiva (Digital double of a forest stand), Mezhdunarodnyj nauchno-issledovatel’skii zhurnal, 2024, No. 6, pp. 1—5.
  98. Zagalikis G., Cameron A.D., Miller D.R., The application of digital photogrammetry and image analysis techniques to derive tree and stand characteristics, Canadian Journal of Forest Research, 2005, Vol. 35, No. 5, pp. 1224—1237.
  99. Zianis D., Mencuccini M., On simplifying allometric analyses of forest biomass, Forest Ecology and Management, 2004, Vol. 187, pp. 311—332.

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