Estimating crown biomass of three coniferous tree species using cross-sectional area at the base of live crown

Keywords: allometric equation, branch biomass, crown biomass, diameter at the base of the live crown, foliage biomass, hinoki cypress, Japanese larch

Abstract

Aim of study: The error in estimating branch biomass, foliage biomass, and crown biomass (i.e. sum of branch and foliage biomass) based on the diameter at the live crown base was evaluated.

Area of study: The foci were hinoki cypress [Chamaecyparis obtusa (Siebold et Zucc.) Endl.], Japanese larch (Larix kaempferi (Lamb.) Carrière), and Sakhalin fir [Abies sachalinensis (Fr.Schmidt) Mast.] in plantation forests located in Japan.

Material and methods: Nineteen cypress trees were destructively sampled in the four stands. Measurements from 64 cypress trees, 44 larch trees, and 27 fir trees were included in the analysis. A power function was applied as an allometric equation to establish the relationship between the diameter at the live crown base and the foliage, branch, and crown biomass. Cross-validation was performed to evaluate the precision and accuracy of biomass estimation for each component using the power function. Crown biomass was estimated from stem volume by using the basic wood density and the biomass expansion factor as a traditional method to compare the error.

Main results: Little bias was detected in the estimation of foliage, branch, or crown biomass. The percent root mean squared error was the lowest for branch biomass (36%, 26%), followed by crown biomass (52%, 26%) and foliage biomass (84%, 34%) for cypress and larch, respectively. These percent root mean squared errors were smaller than those using an allometric equation based on diameter at breast height and total height.

Research highlights: This study indicates the potential of using a single common allometric equation for each species to accurately estimate crown biomass.

Downloads

Download data is not yet available.

References

Abe N, 1981. Studies on the management of Abies sachalinensis Mast. planted forest (III) On biomass estimation of 53-year-old stand. Bull Hokkaido For Exp Stn 19: 115-128. (in Japanese with English summary) https://agriknowledge.affrc.go.jp/RN/2010243965

Affleck DLR, 2019. Aboveground biomass equations for the predominant conifer species of the Inland Northwest USA. For Ecol Manag 432: 179-188. https://doi.org/10.1016/j.foreco.2018.09.009

Aizawa S, Ito E, Hashimoto T, Sakata T, Sakai H, Tanaka N, Takahashi M, Matsuura Y, Sanada M, 2012. Influence of fertilization on growth of 37-years-old plantations of Abies sachalinensis, Picea jezoensis, Picea glehnii and Betula maximowicziana. Boreal For Res 60: 93-99. https://doi.org/10.24494/jfsh.60.0_93

Amateis RL, Burkhart HE, Dunham PH, 1992. Estimating dry weight of dormant-season foliage of loblolly pine. Biomass Bioenergy 3: 319-322. https://doi.org/10.1016/0961-9534(92)90003-9

Dong L, Zhang L, Li F, 2014. A compatible system of biomass equations for three conifer species in Northeast, China. For Ecol Manag 329: 306-317. https://doi.org/10.1016/j.foreco.2014.05.050

Falster DS, Duursma RA, Ishihara MI, Barneche DR, FitzJohn RG, Vårhammar A, Aiba M, Ando M, Anten N, Aspinwall MJ et al., 2015. BAAD: a biomass and allometry database for woody plants. Ecology 96: 1445. https://doi.org/10.1890/14-1889.1

Fernández-Sarría A, López-Cortés I, Martí J, Estornell J, 2022. Estimation of walnut structure parameters using terrestrial photogrammetry based on structure-from-motion (SfM). J Indian Soc Remote Sens 50: 1931-1944. https://doi.org/10.1007/s12524-022-01576-x

Gao Q, Kan J, 2022. Automatic forest DBH measurement based on structure from motion photogrammetry. Remote Sens 14: 2064. https://doi.org/10.3390/rs14092064

Greenhouse Gas Inventory Office of Japan, Ministry of the Environment Japan (eds), 2024. National greenhouse gas inventory report of JAPAN, 2024. Center for Global Environmental Research, Earth System Division, National Institute for Environmental Studies, Japan. https://www.nies.go.jp/gio/en/archive/nir/pi5dm3000010ii0r-att/NID-JPN-2024-v3.0_gioweb.pdf

Hagihara A, Yokota T, Ogawa K, 1993. Allometric relationships in hinoki (Chamaecyparis obtusa (Sieb. et Zucc.) Endl.) trees. Bull Nagoya Univ For 12: 12-29. https://doi.org/10.18999/bulnuf.12.1

Inagaki Y, Nakanishi A, Tange T, 2020. A simple method for leaf and branch biomass estimation in Japanese cedar plantations. Trees 34: 349-356. https://doi.org/10.1007/s00468-019-01920-8

Japan Meteorological Agency, 2022. Mesh climatological normals 2020. https://nlftp.mlit.go.jp/ksj/gml/datalist/KsjTmplt-G02-v3_0.html [23 January 2025]. (in Japanese)

National Land Agency, 1974. 1:20,0000 soil map. https://nlftp.mlit.go.jp/kokjo/inspect/landclassification/land/l_national_map_20-1.html [31 January 2025]. (in Japanese)

Kamei H, Nakajima T, Tatsuhara S, 2024. Evaluation of carbon emission reduction effect of Cryptomeria japonica stand management in Yamagata Prefecture, Japan. Can J For Res 54: 698-711. https://doi.org/10.1139/cjfr-2023-0226

Kershaw JA Jr., Ducey MJ, Beers TW, Husch B, 2017. Forest Mensuration. Fifth edition. Chichester, UK. 613 pp.

Komiyama A, Kato S, Ninomiya I, 2002. Allometric relationships for deciduous broad-leaved forests in Hida District, Gifu Prefecture, Japan. J Jpn For Soc 84: 130-134. (in Japanese with English summary) https://doi.org/10.11519/jjfs1953.84.2_130

Kyoto University, the University of Tokyo, Niigata University, Shishu University, 1963. Busshitsu junkanmen yori mita shinrin seitaikei no seisanryoku: Shiryo No.1 [Productivity of forest ecosystems from the viewpoint of cycle of matter: Data No. 1]. 373 pp. (in Japanese)

Liang X, Kankare V, Hyyppä J, Wang Y, Kukko A, Haggrén H, Yu X, Kaartinen H, Jaakkola A, Guan F et al., 2016. Terrestrial laser scanning in forest inventories. ISPRS J Photogramm Remote Sens 115: 63-77. https://doi.org/10.1016/j.isprsjprs.2016.02.002

Lovell JL, Jupp DLB, Culvenor DS, Coops NC, 2003. Using airborne and ground-based ranging lidar to measure canopy structure in Australian forests. Can J Remote Sen 29: 607-622. https://doi.org/10.5589/m03-014

Menéndez-Miguélez M, Álvarez-Álvarez P, Pardos M, Madrigal G, Ruiz-Peinado R, López-Senespleda E, Del Río M, Calama R, 2023. Development of tools to estimate the contribution of young sweet chestnut plantations to climate-change mitigation. For Ecol Manag 530: 120761. https://doi.org/10.1016/j.foreco.2022.120761

Ormerod DW, 1973. A simple bole model. For Chron 49: 136-138. https://doi.org/10.5558/tfc49136-3

Osawa A, Kurachi N, Matsuura Y, Jomura M, Kanazawa Y, Sanada M, 2005. Testing a method for reconstructing structural development of even-aged Abies sachalinensis stands. Trees 9: 680-693. https://doi.org/10.1007/s00468-005-0432-5

Parresol BR, 2001. Additivity of nonlinear biomass equations. Can J For Res 31: 865-877. https://doi.org/10.1139/x00-202

Raison RJ, Khanna PK, Benson ML, Myers BJ, McMurtrie RE, Lang ARG, 1992. Dynamics of Pinus radiata foliage in relation to water and nitrogen stress: II. Needle loss and temporal changes in total foliage mass. For Ecol Manag 52: 159-178. https://doi.org/10.1016/0378-1127(92)90500-9

R Core Team, 2024. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/ [4 February 2025].

Ritz C, Streibig JC, 2008. Nonlinear Regression with R. Springer, New York, USA, 144 pp.

Saputro GB, Tatsuhara S, Miguchi H, 2003. Allometric equations for estimating root systems of mizunara oak (Quercus crispula) in secondary forests. J For Plann 9: 85-95. https://doi.org/10.20659/JFP.9.2_85

Shinozaki K, Yoda K, Hozumi K, Kira T, 1964a. A quantitative analysis of plant form - the pipe model theory I. Basic analyses. Jpn J Ecol 14: 97-105. https://doi.org/10.18960/seitai.14.3_97

Shinozaki K, Yoda K, Hozumi K, Kira T, 1964b. A quantitative analysis of plant form - the pipe model theory II. Further evidence of the theory and its application in forest ecology. Jpn J Ecol 14: 133-139. https://doi.org/10.18960/seitai.14.4_133

Sumida A, Watanabe T, Miyaura T, 2018. Interannual variability of leaf area index of an evergreen conifer stand was affected by carry-over effects from recent climate conditions. Sci Rep 8: 13590. https://doi.org/10.1038/s41598-018-31672-3

Sumida A, Nakai T, Yamada M, Ono K, Uemura S, Hara T, 2009. Ground-based estimation of leaf area index and vertical distribution of leaf area density in a Betula ermanii forest. Silva Fenn 43: 799-816. https://doi.org/10.14214/sf.174

Tamai S, Ohkubo Y, Tsutsumi T, 1983. Studies on the effects of thinning small-diameter trees (VI) Changes in structure and biomass of a Cryptomeria japonica stand during the twelve years after thinning. J Jpn For Soc 65: 372-381. (in Japanese with English summary) https://doi.org/10.11519/jjfs1953.65.10_372

Tange T, Murakawa I, 1990. Growth and biomass of an 87-year-old manmade Chamaecyparis obutsa stand. Bull Univ Tokyo For 82: 103-112. (in Japanese with English summary) https://repository.dl.itc.u-tokyo.ac.jp/records/25517

Tatsuhara S, 2012. Comparison of regression methods for fitting allometric equations to biomass of mizunara oak (Quercus crispula). J For Plann 18: 41-52. https://doi.org/10.20659/jfp.18.1_41

Tziaferidis SR, Spyroglou G, Fotelli MN, Radoglou K, 2022. Allometric models for the estimation of foliage area and biomass from stem metrics in black locust. iForest - Biogeosciences and Forestry 15: 281-288. https://doi.org/10.3832/ifor3939-015

Verra, 2023. VCS Methodology. VM0003: Methodology for improved forest management through extension of rotation age. Version 1.3. https://verra.org/wp-content/uploads/2023/05/VM0003-IFM-Through-Extension-Of-Rotation-Age-v1.3.pdf [25 June 2024].

Yamakura T, Saito H, Shidei T, 1972. Production and structure of under-ground part of hinoki (Chamaecyparis obtusa) stand (I) Estimation of root production by means of root analysis. J Jpn For Soc 54: 118-125. https://doi.org/10.11519/jjfs1953.54.4_118

Yu X, Liang X, Hyyppä J, Kankare V, Vastaranta M, Holopainen M, 2013. Stem biomass estimation based on stem reconstruction from terrestrial laser scanning point clouds. Remote Sens Lett 4: 344-353. https://doi.org/10.1080/2150704X.2012.734931

Published
2026-03-06
How to Cite
Tatsuhara, S. (2026). Estimating crown biomass of three coniferous tree species using cross-sectional area at the base of live crown. Forest Systems, 34(2), 20976. https://doi.org/10.5424/fs/2025342-20976
Section
Research Articles