Constructing a non-linear additive crown-width model system for moso bamboo forests in eastern China

References

• Akinwande MO, Dikko HG, Samson A. 2015. Variance inflation factor: as a condition for the inclusion of suppressor variable(s) in regression analysis. Open Journal of Statistics. 5(7):754–767. doi:10.4236/ojs.2015.57075.
   Google Scholar
• Azuma D, Monleon VJ, Gedney D. 2004. Equations for predicting uncompacted crown ratio based on compacted crown ratio and tree attributes. Western Journal of Applied Forestry. 19(4):260–267. doi:10.1093/wjaf/19.4.260.
   Google Scholar
• Bechtold WA. 2003. Crown-diameter prediction models for 87 species of stand-grown trees in the Eastern United States. Southern Journal of Applied Forestry. 27(4):269–278. doi:10.1093/sjaf/27.4.269.
   Google Scholar
• Bi H, Birk E, Turner J, Lambert M. 2001. Converting stem volume to biomass with additivity, bias correction, and confidence bands for two Australian tree species. New Zealand Journal of Forestry Science. 31:298–319.
   Google Scholar
• Biging GS, Dobbertin M. 1992. A comparison of distance-dependent competition measures for height and basal area growth of individual conifer trees. Forestry Sciences. 38(3):695–720.
   Web of Science ®Google Scholar
• Biging GS, Dobbertin M. 1995. Evaluation of competition indices in individual tree growth models. Forest Science. 41(2):366–377.
   Web of Science ®Google Scholar
• Bohlman S, Pacala S. 2012. A forest structure model that determines crown layers and partitions growth and mortality rates for landscape-scale applications of tropical forests. Journal of Ecology. 100(2):508–518. doi:10.2307/41496099.
   Web of Science ®Google Scholar
• Bragg D. 2001. A local basal area adjustment for crown width prediction. Northern Journal of Applied Forestry. 18(1):22–28. doi:10.1093/njaf/18.1.22.
   Google Scholar
• Canavan SJ, Ramm CW. 2000. Accuracy and precision of 10 year predictions for forest vegetation simulator—Lake States. Northern Journal of Applied Forestry. 17(2):62–70. doi:10.1093/njaf/17.2.62.
   Google Scholar
• Carvalho JP, Parresol BR. 2003. Additivity in tree biomass components of pyrenean oak (Quercus pyrenaica Willd.). Forest Ecology and Management. 179(1–3):269–276. doi:10.1016/S0378-1127(02)00549-2.
   Web of Science ®Google Scholar
• Ding Y, Zang R. 2021. Determinants of aboveground biomass in forests across three climatic zones in China. Forest Ecology and Management. 482:118805. doi:10.1016/j.foreco.2020.118805.
   Web of Science ®Google Scholar
• Feng ZK, Liu YX. 2005. Precision analysis of forest biomass measurements. Journal of Beijing Forest University. S2:108–111.
   Google Scholar
• Fu L, Lei Y, Wang G, Bi H, Tang S, Song X. 2016. Comparison of seemingly unrelated regressions with error-in-variable models for developing a system of nonlinear additive biomass equations. Trees. 30(3):839–857. doi:10.1007/s00468-015-1325-x.
   Web of Science ®Google Scholar
• Fu L, Liu Q, Sun H, Wang Q, Li Z, Chen E, Pang Y, Song X, Wang G. 2018. 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. 10(2):325. doi:10.3390/rs10020325.
   Web of Science ®Google Scholar
• Fuller WA. 1987. Measurement error models. New York: Wiley.
   Google Scholar
• Fu L, Sharma RP, Hao K, Tang S. 2017. A generalized interregional nonlinear mixed-effects crown width model for Prince Rupprecht larch in northern China. Forest Ecology and Management. 389:364–373. doi:10.1016/j.foreco.2016.12.034.
   Web of Science ®Google Scholar
• Fu L, Sharma RP, Wang G, Tang S. 2017. Modelling a system of nonlinear additive crown width models applying seemingly unrelated regression for prince Rupprecht larch in northern China. Forest Ecology and Management. 386:71–80. doi:10.1016/j.foreco.2016.11.038.
   Web of Science ®Google Scholar
• Fu L, Sun H. 2013. Individual crown diameter prediction for Cunninghamia lanceolata forests based on mixed effects models. Scientia Silvae Sinicae. 49:65–74. doi:10.11707/j.1001-7488.20130810.
   Google Scholar
• Fu L, Sun H, Sharma RP, Lei Y, Zhang H, Tang S. 2013. Nonlinear mixed-effects crown width models for individual trees of Chinese fir (Cunninghamia lanceolata) in south-central China. Forest Ecology and Management. 302:210–220. doi:10.1016/j.foreco.2013.03.036.
   Web of Science ®Google Scholar
• Fu L, Xiang W, Wang G, Hao K, Tang S. 2017. Additive crown width models comprising nonlinear simultaneous equations for Prince Rupprecht larch (Larix principis-rupprechtii) in northern China. Trees. 31(6):1959–1971. doi:10.1007/s00468-017-1600-0.
   Web of Science ®Google Scholar
• Fu L, Zhang H, Lu J, Zang H, Lou M, Wang G. 2015. Multilevel nonlinear mixed-effect crown ratio models for individual trees of Mongolian oak (Quercus mongolica) in Northeast China. PLoS One. 10(8):e0133294. doi:10.1371/journal.pone.0133294.
   PubMed Web of Science ®Google Scholar
• Fu L, Zhang H, Sharma RP, Pang L, Wang G. 2017. A generalized nonlinear mixed-effects height to crown base model for Mongolian oak in northeast China. Forest Ecology and Management. 384:34–43. doi:10.1016/j.foreco.2016.09.012.
   Web of Science ®Google Scholar
• Gertner G. 2011. The sensitivity of measurement error in stand volume estimation. Canadian Journal of Forest Research. 20(6):800–804. doi:10.1139/x90-105.
   Google Scholar
• Hao X, Yujun S, Xinjie W, Wang J, Yao F. 2015. Linear mixed-effects models to describe individual tree crown width for China-fir in Fujian Province, southeast China. PLoS One. 10(4):e0122257. doi:10.1371/journal.pone.0122257.
   PubMed Web of Science ®Google Scholar
• Hasenauer H, Monserud RA. 1997. Biased predictions for tree height increment models developed from smoothed ‘data’. Ecological Modelling. 98(1):13–22. doi:10.1016/S0304-3800(96)01933-3.
   Web of Science ®Google Scholar
• Huang LY. 2021. Study on spatial distribution characteristics and dynamics of moso bamboo forests under different strip clearcutting based on terrestrial laser scanning. [master’s thesis]. Beijing (China): Chinese Academy of Forestry.
   Google Scholar
• Huang S, Price DJ, Titus S. 2000. Development of ecoregion-based height–diameter models for white spruce in boreal forests. Forest Ecology and Management. 129:125–141. doi:10.1016/S0378-1127(99)00151-6.
   Web of Science ®Google Scholar
• Hynynen J, Ojansuu R, Hökkä H, Siipilehto J, Salminen H, Haapala P. 2002. Models for predicting stand development in MELA system. Finnish Forest Research Institute Research Papers. 835:1–116.
   Google Scholar
• Lei Y, Fu L, Affleck DLR, Nelson AS, Shen C, Wang M, Zheng J, Ye Q, Yang G. 2018. Additivity of nonlinear tree crown width models: aggregated and disaggregated model structures using nonlinear simultaneous equations. Forest Ecology and Management. 427:372–382. doi:10.1016/j.foreco.2018.06.013.
   Web of Science ®Google Scholar
• Leites L, Robinson A, Crookston N. 2009. Accuracy and equivalence testing of crown ratio models and assessment of their impact on diameter growth and basal area increment predictions of two variants of the forest vegetation simulator. Canadian Journal of Forest Research. 39(3):655–665. doi:10.1139/X08-205.
   Web of Science ®Google Scholar
• Liu J, Jiang PK, Wang HL, Zhou GM, Wu JS, Yang F, Qian XB. 2011. Seasonal soil CO2 efflux dynamics after land use change from a natural forest to moso bamboo plantations in subtropical China. Forest Ecology and Management. 262(6):1131–1137. doi:10.1016/j.foreco.2011.06.015.
   Web of Science ®Google Scholar
• Marshall D, Johnson G, Hann D. 2003. Crown profile equations for stand-grown western hemlock trees in northwestern Oregon. Canadian Journal of Forest Research. 33(11):2059–2066. doi:10.1139/x03-126.
   Web of Science ®Google Scholar
• McRoberts R, Westfall J. 2014. Effects of uncertainty in model predictions of individual tree volume on large area volume estimates. Forest Science. 60(1):34–42. doi:10.5849/forsci.12-141.
   Web of Science ®Google Scholar
• Monserud RA, Sterba H. 1996. A basal area increment model for individual trees growing in even- and uneven-aged forest stands in Austria. Forest Ecology and Management. 80(1–3):57–80. doi:10.1016/0378-1127(95)03638-5.
   Web of Science ®Google Scholar
• National Forestry and Grassland Administration. 2019. China Forest Resources Report 2014–2018. Beijing (China): China Forestry Publishing House.
   Google Scholar
• Omule SAY. 1980. Personal bias in forest measurements. Forestry Chronicle. 56(5):222–224. doi:10.5558/tfc56222-5.
   Web of Science ®Google Scholar
• Parresol BR. 1999. Assessing tree and stand biomass: a review with examples and critical comparisons. Forest Science. 45:573–593. doi:10.1093/forestscience/45.4.573.
   Web of Science ®Google Scholar
• Parresol BR. 2001. Additivity of nonlinear biomass equations. Canadian Journal of Forest Research. 31(5):865–878. doi:10.1139/x00-202.
   Web of Science ®Google Scholar
• Power H, LeMay V, Berninger F, Sattler D, Kneeshaw D. 2012. Differences in crown characteristics between black (Picea mariana) and white spruce (Picea glauca). Canadian Journal of Forest. 42(9):1733–1743. doi:10.1139/x2012-106.
   Web of Science ®Google Scholar
• Pretzsch H, Biber P, Ďurský J. 2002. The single tree-based stand simulator SILVA: construction, application and evaluation. Forest Ecology and Management. 162(1):3–21. doi:10.1016/S0378-1127(02)00047-6.
   Web of Science ®Google Scholar
• Pukkala T, Becker P, Kuuluvainen T, Oker-Blom P. 1991. Predicting spatial distribution of direct radiation below forest canopies. Agricultural and Forest Meteorology. 55(3–4):295–307. doi:10.1016/0168-1923(91)90067-Z.
   Web of Science ®Google Scholar
• R Core Team. 2022. R: a language and environment for statistical computing. Vienna (Austria): R Foundation for Statistical Computing. http://www.R-project.org/.
   Google Scholar
• Rencher AC, Schaalje GB. 2008. Linear models in statistics, 2nd ed. New York: Wiley.
   Google Scholar
• Roeh RL, Maguire DA. 1997. Crown profile models based on branch attributes in coastal douglas-fir. Forest Ecology and Management. 96(1–2):77–100. doi:10.1016/S0378-1127(97)00033-9.
   Web of Science ®Google Scholar
• Russell MB, Weiskittel AR. 2011. Maximum and largest crown width equations for 15 tree species in Maine. Northern Journal of Applied Forestry. 28(2):84–91. doi:10.1093/njaf/28.2.84.
   Google Scholar
• Sánchez-González M, Cañellas I, Montero G. 2007. Generalized height-diameter and crown diameter prediction models for cork oak forests in Spain. Investigación Agraria: Sistemas y Recursos Forestales. 16(1):76. doi:10.5424/srf/2007161-00999.
   Google Scholar
• Sharma RP, Vacek Z, Vacek S. 2016. Individual tree crown width models for Norway spruce and European beech in Czech Republic. Forest Ecology and Management. 366:208–220. doi:10.1016/j.foreco.2016.01.040.
   Web of Science ®Google Scholar
• Soares P, Tomé M. 2001. A tree crown ratio prediction equation for eucalypt plantations. Annals of Forest Science. 58(2):193–202. doi:10.1051/forest:2001118.
   Web of Science ®Google Scholar
• Song XZ, Zhou GM, Jiang H, Yu SQ, Fu JH, Li WZ, Wang WF, Ma ZH, Peng CH. 2011. Carbon sequestration by Chinese bamboo forests and their ecological benefits: assessment of potential, problems, and future challenges. Environmental Reviews. 19:418–428. doi:10.1139/a11-015.
   Web of Science ®Google Scholar
• Sonmez T. 2009. Diameter at breast height-crown diameter prediction models for Picea orientalis. African Journal of Agricultural Research. 4(3):215−219.
   Google Scholar
• Stenberg P, Pukkala T, Kuuluvainen T. 1989. Relationship between radiation interception and photosynthesis in forest canopies: effect of stand structure and latitude. Ecological Modelling. 49(1–2):73–87. doi:10.1016/0304-3800(89)90044-6.
   Web of Science ®Google Scholar
• Tahvanainen T, Forss E. 2008. Individual tree models for the crown biomass distribution of scots pine, Norway spruce and birch in Finland. Forest Ecology and Management. 255(3–4):455–467. doi:10.1016/j.foreco.2007.09.035.
   Web of Science ®Google Scholar
• Tang S, Li Y. 2002. Statistical foundation for biomathematical models. Beijing (China): Science Press. Chinese.
   Google Scholar
• Tang X, Fan S, Qi L, Guan F, Du M, Zhang H. 2016. Soil respiration and net ecosystem production in relation to intensive management in moso bamboo forests. Catena. 137:219–228. doi:10.1016/j.catena.2015.09.008.
   Web of Science ®Google Scholar
• Tang SZ, Li Y. 1996. An algorithm for estimating multivariate non-linear error-in-measure models. Journal of Biomathematics. 11:23–27. Chinese. doi:10.1007/BF02951625.
   Google Scholar
• Tang S, Li Y, Wang Y. 2001. Simultaneous equations, errors-in-variable models, and model integration in systems ecology. Ecological Modelling. 142(3):285–294. doi:10.1016/S0304-3800(01)00326-X.
   Web of Science ®Google Scholar
• Toney C, Reeves MC. 2009. Equations to convert compacted crown ratio to uncompacted crown ratio for trees in the interior west. Western Journal of Applied Forestry. 24(2):76–82. doi:10.1093/wjaf/24.2.76.
   Google Scholar
• Wang B, Wei W, Liu C, You W, Niu X, Man R. 2013. Biomass and carbon stock in moso bamboo forests in subtropical China: characteristics and implications. Journal of Tropical Forest Science. 25(1):137–148.
   Web of Science ®Google Scholar
• Wang ZJ, Li FY, Yan DF, Li SC, Zhang LL. 2009. Study on crown width, crown length and growth of different Populus tomentosa clones. Journal of Henan Institute of Science and Technology. 4:14–18. Chinese.
   Google Scholar
• Yen T-M. 2016. Culm height development, biomass accumulation and carbon storage in an initial growth stage for a fast-growing moso bamboo (Phyllostachy pubescens). Botanical Studies. 57(1):10. doi:10.1186/s40529-016-0126-x.
   PubMed Web of Science ®Google Scholar
• Yen T-M, Lee J-S. 2011. Comparing aboveground carbon sequestration between moso bamboo (Phyllostachys heterocycla) and China fir (Cunninghamia lanceolata) forests based on the allometric model. Forest Ecology and Management. 261(6):995–1002. doi:10.1016/j.foreco.2010.12.015.
   Web of Science ®Google Scholar
• Zarnoch S, Bechtold W, Stolte K. 2004. Using crown condition variables as indicators of forest health. Canadian Journal of Forest Research. 34(5):1057–1070. doi:10.1139/x03-277.
   Web of Science ®Google Scholar
• Zeng WS, Tang SZ. 2010. Using measurement error modeling method to establish compatible single-tree biomass equations system. Forest Research. 23:797–802. Chinese with English abstract.
   Google Scholar
• Zhou X, Zheng Y, Guan F, Xiao X, Zhang X, Li C. 2022. Compatible biomass model of moso bamboo with measurement error. Forests. 13(5):774. doi:10.3390/f13050774.
   Web of Science ®Google Scholar