Biomass and Volume Estimation Models for Bark of Small-Leaved Linden (Tilia cordata Mill.)

Authors

DOI:

https://doi.org/10.37482/0536-1036-2022-5-21-36

Keywords:

trunk diameter at breast height, tree height, bark biomass, trunk volume, bark volume, bark proportion, small-leaved linden, modeling, errors, bark biomass estimation

Abstract

The research is aimed at analyzing variability and developing mathematical models for estimating bark biomass and volume, volume of trunk with bark, and bark proportion of small-leaved linden (Tilia cordata Mill.) trees growing in natural coppice and artificial stands. The models are based on data from 107 and 95 destructively sampled trees in natural coppice and artificial stands, respectively. There were 10 sampling areas per stand type, representing different growth stages. The model trees were sawn into 2-meter sections, the volumes of which with and without bark were calculated using the Huber formula. The total volume of the tree trunk with and without bark is obtained by summing the volumes of all sections and the conical volume of the tree top. The bark volume was the difference between these two parameters. The bark biomass was determined by direct weighing, followed by conversion to absolutely dry mass. The correlations between the dendrometric parameters and the selected tree characteristics were estimated. The effectiveness of 3 regression models using the diameter at breast height (dbh = 1.3 m) and the tree height (h) as independent variables was studied in a comparative aspect. The bark biomass and volume, and the volume of trunk with bark are strongly influenced by these values. This correlation is very weak for the bark volume proportion in natural coppice stands, and insignificant in artificial stands. The bark volume proportion for each tree was calculated as the ratio of the difference between the volume of trunk with and without bark and the volume of trunk with bark. The equation that showed the best statistical characteristics in terms of consistency was chosen in order to predict the bark biomass and volume, the volume of trunks with bark of small-leaved linden trees. These models were estimated using the weighted least squares method taking into account the inherent errors and heteroscedasticity, by assigning each model its weight function separately for natural coppice and artificial stands that differ significantly from each other in morphometric features.

For citaton: Gabdelkhakov A.K., Konovalov V.F., Rakhmatullin Z.Z., Blonskaya L.N., Fazlutdinov I.I. Biomass and Volume Estimation Models for Bark of Small-Leaved Linden (Tilia cordata Mill.). Lesnoy Zhurnal = Russian Forestry Journal, 2022, no. 5, pp. 21–36. (In Russ.). https://doi.org/10.37482/0536-1036-2022-5-21-36

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Author Biographies

Aydar K. Gabdelkhakov, Bashkir State Agrarian University

Candidate of Agriculture, Assoc. Prof.

Vladimir F. Konovalov, Bashkir State Agrarian University

Doctor of Agriculture, Prof.

 

Zagir Z. Rakhmatullin, Bashkir State Agrarian University

Candidate of Agriculture

 

Liubov N. Blonskaya, Bashkir State Agrarian University

Candidate of Biology, Assoc. Prof.

 

Ilyas I. Fazlutdinov, Ministry of Forestry of the Republic of Bashkortostan

Leading Specialist

 

References

Габделхаков А.К., Рахматуллин З.З., Мартынова М.В., Фазлутдинов И.И., Муллагалеев И.А. Процент коры деревьев липы мелколистной (Tilia cordata Mill.) по сортиментным таблицам // Рос. электрон. науч. журн. 2021. № 2(40). С. 121–130. Gabdelkhakov A.K., Rakhmatullin Z.Z., Martynova M.V., Fazlutdinov I.I., Mullagaleev I.A. Percentage of Bark of Small-Leaved Linden (Tilia cordata Mill.) Trees According to Yield Tables. Russian electronic scientific journal, 2021, vol. 2(40), pp. 121–130. (In Russ.). https://doi.org/10.31563/2308-9644-2021-40-2-121-130

Орлова И.В., Филонова Е.С. Выбор экзогенных факторов в модель регрессии при мультиколлинеарности данных // Междунар. журн. прикладных и фундаментальных исследований. 2015. № 5, ч. 1. С. 108–116. Orlova I.V., Filonova E.S. The Choice of Exogenous Factors in the Regression Model with Multicollinearity in the Data. International Journal of Applied and Fundamental Research, 2015, no. 5, part 1, pp. 108–116. (In Russ.).

Орловская Т.В., Гюльбякова Х.Н., Гужва Н.Н., Огурцов Ю.А. Изучение коры липы сердцелистной с целью создания новых лекарственных средств // Современные проблемы науки и образования. 2013. № 2. C. 427. Режим доступа: https://science-educa- tion.ru/ru/article/view?id=8561 (дата обращения: 31.03.22). Orlovskaya T.V., Gulbjakova C.N., Gujva N.N., Ogurtcov Y.A. Studying the Tilia cordata L. Bark with the Purpose of Creation the New Medicines. Modern problems of science and education, 2013, no. 2, p. 427. (In Russ.).

Хасанова З.Ф. Лесные промыслы башкир Инзерского бассейна (конец XIX в. – начало XXI в.) // История, археология и этнография Кавказа. 2018. Т. 14, № 1. С. 105–112. Hasanova Z.F. Forest Works of the Bashkir of the Inzerian Basin (End of the 19th – the Beginning of the 21st Centuries). History, Archeology and Ethnography of the Caucasus, 2018, vol. 14, no. 1, pp. 105–112. (In Russ.). https://doi.org/10.24411/2618-6772-2018-10113

Akintunde M.O., Olawale A.O., Amusan A.S., Azeez A.I.A. Comparing Two Classical Methods of Detecting Multicollinearity in Financial and Economic Time Series Data. International Journal of Applied Mathematics and Theoretical Physics, 2021, vol. 7, no. 3, pp. 62–67. https://doi.org/10.11648/j.ijamtp.20210703.11

Avila A.L., Albrecht A. Alternative Baumarten im Klimawandel: Artensteckbriefe – eine Stoffsammlung. Baden-Württemberg, Forstliche Versuchsund Forschungsanstalt (FVA), 2018. 123 p. (In Ger.). Available at: https://www.fva-bw.de/fileadmin/publikationen/ sonstiges/180201steckbrief.pdf (accessed 31.03.22).

Bauer R., Billard A., Mothe F., Longuetaud F., Houballah M., Bouvet A., Cuny H., Colin A., Colin F. Modelling Bark Volume for Six Commercially Important Tree Species in France: Assessment of Models and Application at Regional Scale. Annals of Forest Science, 2021, vol. 78, art. 104. https://doi.org/10.1007/s13595-021-01096-7

Bijak S., Bronisz A., Bronisz K., Tomusiak R., Wojtan R., Baran P., Czemiel T., Zasada M. Models to Estimate the Bark Volume for Larix sp. in Poland. Environmental Sciences Proceedings, 2021, vol. 3, iss. 1, art. 71. https://doi.org/10.3390/IECF2020-07915

Božić M., Čavlović J., Vedriš M., Jazbec M. Modeling Bark Thickness of Silver Fir Trees (Abies alba Mill.). Šumarski list, 2007, vol. 131, no. 1-2, pp. 3–12. (In Croat.). https://doi.org/10.31298/sl

Breusch T.S., Pagan A.R. A Simple Test for Heteroscedasticity and Random Coefficient Variation. Econometrica, 1979, vol. 47, no. 5, pp. 1287–1294. https://doi. org/10.2307/1911963

Canga E., Diéguez-Aranda U., Elias A.K., Cámara A. Above-Ground Biomass Equations for Pinus radiata D. Don in Asturias. Forest Systems, 2013, vol. 22, no. 3, pp. 408–415. https://doi.org/10.5424/fs/2013223-04143

Cellini J.M., Galarza M., Burns S.L., Martinez-Pastur G.J., Lencinas M.V. Equations of Bark Thickness and Volume Profiles at Different Heights with Easy-Measurement Variables. Forest Systems, 2012, vol. 21, no. 1, pp. 23–30. https://doi.org/10.5424/fs/2112211-01963

Chen D., Huang X., Zhang S., Sun X. Biomass Modeling of Larch (Larix spp.) Plantations in China Based on the Mixed Model, Dummy Variable Model, and Bayesian Hierarchical Model. Forests, 2017, vol. 8, iss. 8, art. 268. https://doi.org/10.3390/f8080268

Diamantopoulou M.J., Özçelik R., Yavuz H. Tree-Bark Volume Prediction via Machine Learning: A Case Study Based on Black Alder’s Tree-Bark Production. Computers and Electronics in Agriculture, 2018, vol. 151, pp. 431–440. https://doi.org/10.1016/j.com- pag.2018.06.039

Eaton E., Caudullo G., de Rigo D. Tilia cordata, Tilia platyphyllos and Other Limes in Europe: Distribution, Habitat, Usage and Threats. European Atlas of Forest Tree Species. Ed. by J. San-Miguel-Ayanz, D. de Rigo, G. Caudullo, T. Houston Durrant, A. Mauri. Luxembourg, Publications Office of the EU, 2016, pp. 184–185. Available at: https://forest. jrc.ec.europa.eu/media/atlas/Tilia_spp.pdf (accessed 31.03.22).

Guéguen F., Stille P., Lahd Geagea M., Boutin R. Atmospheric Pollution in an Urban Environment by Tree Bark Biomonitoring – Part I: Trace Element Analysis. Chemosphere, 2012, vol. 86, iss. 10, pp. 1013–1019. https://doi.org/10.1016/j.chemosphere.2011.11.040

Heath L.S., Hansen M., Smith J.E., Smith B.W., Miles P.D. Investigation into Calculating Tree Biomass and Carbon in the FIADB Using a Biomass Expansion Factor Approach. Forest Inventory and Analysis (FIA) Symposium 2008. Park City, UT, USDA, 2009. 26 р.

Hemery G., Spiecker H., Aldinger E., Kerr G., Collet C., Bell S. COST Action E42: Growing Valuable Broadleaved Tree Species. Final Report. 2008. 40 p.

Kim J.H. Multicollinearity and Misleading Statistical Results. Korean Journal of Anesthesiology, 2019, vol. 72(6), pp. 558–569. https://doi.org/10.4097/kja.19087

Kohnle U., Hein S., Sorensen F.C., Weiskittel A.R. Effects of Seed Source Origin on Bark Thickness of Douglas-fir (Pseudotsuga menziesii) Growing in Southwestern Germany. Canadian Journal of Forest Research, 2012, no. 42(2), pp. 382–399. https://doi. org/10.1139/X11-191

Laasasenaho J., Melkas T., Aldén S. Modelling Bark Thickness of Picea abies with Taper Curves. Forest Ecology and Management, 2005, vol. 206, iss. 1-3, pp. 35–47. https://doi.org/10.1016/j.foreco.2004.10.058

Lestander T.A., Lundström A., Finell M. Assessment of Biomass Functions for Calculating Bark Proportions and Ash Contents of Refined Biomass Fuels Derived from Major Boreal Tree Species. Canadian Journal of Forest Research, 2012, vol. 42, no. 1, pp. 59–66. https://doi.org/10.1139/x11-144

Liepiņš J., Liepiņš K. Evaluation of Bark Volume of Four Tree Species in Latvia. Research for Rural Development, 2015, vol. 2, pp. 22–28.

Magalhães T.M. Effects of Site and Tree Size on Wood Density and Bark Properties of Lebombo Ironwood (Androstachys johnsonii Prain). New Zealand Journal of Forestry Science, 2021, vol. 51, art. 3. https://doi.org/10.33494/nzjfs512021x32x

Meng S., Jia Q., Liu Q., Zhou G., Wang H., Yu J. Aboveground Biomass Allocation and Additive Allometric Models for Natural Larix gmelinii in the Western Daxing’anling Mountains, Northeastern China. Forests, 2019, vol. 10, iss. 2, art. 150. https://doi.org/10.3390/f10020150

Meng S., Yang F., Hu S., Wang H., Wang H. Generic Additive Allometric Models and Biomass Allocation for Two Natural Oak Species in Northeastern China. Forests, 2021, vol. 12, iss. 6, art. 715. https://doi.org/10.3390/f12060715

Myking T., Hertzberg A., Skrøppa T. History, Manufacture and Properties of Lime Bast Cordage in Northern Europe. Forestry, 2005, vol. 78, iss. 1, pp. 65–71. https://doi. org/10.1093/forestry/cpi006

Neumann M., Lawes M.J. Quantifying Carbon in Tree Bark: The Importance of Bark Morphology and Tree Size. Methods in Ecology and Evolution, 2021, no. 12, iss. 4, pp. 646–654. https://doi.org/10.1111/2041-210X.13546

Pásztory Z., Mohácsiné I.R., Gorbacheva G., Börcsök Z. The Utilization of Tree Bark. BioResources, 2016, vol. 11(3), pp. 7859–7888. https://doi.org/10.15376/biores.11.3

Radoglou K., Dobrowolska D., Spyroglou G., Nicolescu V.-N. A Review on the Ecology and Silviculture of Limes (Tilia cordata Mill., Tilia platyphyllos Scop. and Tilia tomentosa Moench.) in Europe. Die BodenKultur, 2009, vol. 60, no. 3, pp. 9–19.

Repola J. Biomass Equations for Scots Pine and Norway Spruce in Finland. Silva Fennica, 2009, vol. 43, no. 4, pp. 625–647. https://doi.org/10.14214/sf.184

Saint-Andre L., M’Bou A.T., Mabiala A., Mouvondy W., Jourdan C., Roupsard O., Deleporte P., Hamel O., Nouvellon Y. Age-Related Equations for Aboveand Below-Ground Biomass of a Eucalyptus Hybrid in Congo. Forest Ecology and Management, 2005, vol. 205, iss. 1-3, pp. 199–214. https://doi.org/10.1016/j.foreco.2004.10.006

Samojlik T. Drzewo wielce użyteczne – historia lipy drobnolistnej (Tilia cordata) w Puszczy Białowieskiej. Rocznik Dendrologiczny, 2005, vol. 53, pp. 55–64. (In Pol.).

Schmidt O., Buβler H. Die Winterlinde als Lebensraum für Tierarten. LWF Wissen, 2016, no. 78, pp. 60–65. (In Ger.).

Sedmíková M., Löwe R., Jankovský M., Natov P., Linda R., Dvořák J. Estimation of Overand Under-Bark Volume of Scots Pine Timber Produced by Harvesters. Forests, 2020, vol. 11, iss. 6, art. 626. https://doi.org/10.3390/f11060626

Sharma R.P., Bhandari S.K., BK R. Allometric Bark Biomass Model for Daphne bholua in the Mid-Hills of Nepal. Mountain Research and Development, 2017, vol. 37, no. 2, pp. 206–215. https://doi.org/10.1659/mrd-journal-d-16-00052.1

Sonmez T., Keles S., Tilki F. Effect of Aspect, Tree Age and Tree Diameter on Bark Thickness of Picea orientalis. Scandinavian Journal of Forest Research, 2007, vol. 22, iss. 3, pp. 193–197. https://doi.org/10.1080/02827580701314716

Stängle S.M., Sauter U.H., Dormann C.F. Comparison of Models for Estimating Bark Thickness of Picea abies in Southwest Germany: The Role of Tree, Stand, and Environmental Factors. Annals of Forest Science, 2017, vol. 74, art. 16. https://doi.org/10.1007/ s13595-016-0601-2

Subedi M., Sharma R.P. Allometric Biomass Models for Bark of Cinnamomum tamala in Mid-Hill of Nepal. Biomass and Bioenergy, 2012, vol. 47, pp. 44–49.

Temesgen H., Affleck D., Poudel K., Gray A., Sessions J. A Review of the Challenges and Opportunities in Estimating Above Ground Forest Biomass Using Tree-Level Models. Scandinavian Journal of Forest Research, 2015, vol. 30, iss. 4, pp. 326–335. https://doi.org/10.1080/02827581.2015.1012114

Vacek S., Vacek Z., Ulbrichová I., Bulušek D., Prokůpková A., Král J., Vančura K. Biodiversity Dynamics of Differently Managed Lowland Forests Left to Spontaneous Development in Central Europe. Austrian Journal of Forest Science, 2019, vol. 136, iss. 3, pp. 249–281.

Vezzola L.C., Muttoni G., Merlini M., Rotiroti N., Pagliardini L., Hirt A.M., Pelfini M. Investigating Distribution Patterns of Airborne Magnetic Grains Trapped in Tree Barks in Milan, Italy: Insights for Pollution Mitigation Strategies. Geophysical Journal International, 2017, vol. 210, iss. 2, pp. 989–1000. https://doi.org/10.1093/gji/ggx232

Wang X., Zhao D., Liu G., Yang C., Teskey R.O. Additive Tree Biomass Equations for Betula platyphylla Suk. Plantations in Northeast China. Annals of Forest Science, 2018, vol. 75, art. 60. https://doi.org/10.1007/s13595-018-0738-2

Wehenkel C., Cruz-Cobos F., Carrillo A., Lujan-Soto J.E. Estimating Bark Volumes for 16 Native Tree Species on the Sierra Madre Occidental, Mexico. Scandinavian Journal of Forest Research, 2012, vol. 27, iss. 6, pp. 578–585. https://doi.org/10.1080/02827581.2012.661453

Wilhelmsson L., Arlinger J., Spångberg K., Lundqvist S.-O., Grahn T., Hedenberg Ö., Olsson L. Models for Predicting Wood Properties in Stems of Picea abies and Pinus sylvestris in Sweden. Scandinavian Journal of Forest Research, 2002, vol. 17, iss. 4, pp. 330–350. https://doi.org/10.1080/02827580260138080

Zeng W.S., Tang S.Z. Bias Correction in Logarithmic Regression and Comparison with Weighted Regression for Nonlinear Models. Nature Precedings, 2011. 11 p. https://doi.org/10.1038/npre.2011.6708.1

Published

2022-10-28

How to Cite

Gabdelkhakov А. ., Konovalov В. ., Rakhmatullin З. ., Blonskaya Л., and Fazlutdinov И. . “Biomass and Volume Estimation Models for Bark of Small-Leaved Linden (Tilia Cordata Mill.)”. Lesnoy Zhurnal (Forestry Journal), no. 5, Oct. 2022, pp. 21-36, doi:10.37482/0536-1036-2022-5-21-36.