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Forest Ecosystems 2022, 9(3): 100037 https://doi.org/10.1016/j.fecs.2022.100037
Research Article | Open Access | Issue | Published: 21 April 2022

Using machine learning algorithms to estimate stand volume growth of Larix and Quercus forests based on national-scale Forest Inventory data in China

Show Author's Information Huiling Tiana,  Jianhua Zhua,b,*( ),  Xiao Hec,  Xinyun Chend,  Zunji Jiana,  Chenyu Lia,  Qiangxin Oue,  Qi Lia,  Guosheng Huangd,  Changfu Liua,b,  Wenfa Xiaoa,b,**( )
Ecology and Nature Conservation Institute, Chinese Academy of Forestry, Key Laboratory of Forest Ecology and Environment of National Forestry and Grassland Administration, Beijing, 100091, China
Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, 210037, China
Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Key Laboratory of Forest Management and Growth Modeling, National Forestry and Grassland Administration, Beijing, 100091, China
Academy of Forest Inventory and Planning, National Forestry and Grassland Administration, Beijing, 100714, China
School of Forestry and Landscape Architecture, Anhui Agricultural University, Hefei, 230036, Anhui, China

* Corresponding author. Institute of Forest Ecology, Environment and Nature Conservation, Chinese Academy of Forestry, No. 1 of Dongxiaofu, Xiangshan Road, Haidian District, Beijing, 100091, China.

** Corresponding author. Ecology and Nature Conservation Institute, Chinese Academy of Forestry, Key Laboratory of Forest Ecology and Environment of National Forestry and Grassland Administration, Beijing, 100091, China.

Keywords:
Stand volume growth, Stand origin, Plant functional type, National forest inventory data, Random forest algorithms
Cite this article:
Tian H, Zhu J, He X, et al. Using machine learning algorithms to estimate stand volume growth of Larix and Quercus forests based on national-scale Forest Inventory data in China. Forest Ecosystems, 2022, 9(3): 100037. https://doi.org/10.1016/j.fecs.2022.100037
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Abstract Full text About this article

Estimating the volume growth of forest ecosystems accurately is important for understanding carbon sequestration and achieving carbon neutrality goals. However, the key environmental factors affecting volume growth differ across various scales and plant functional types. This study was, therefore, conducted to estimate the volume growth of Larix and Quercus forests based on national-scale forestry inventory data in China and its influencing factors using random forest algorithms. The results showed that the model performances of volume growth in natural forests (R2 ​= ​0.65 for Larix and 0.66 for Quercus, respectively) were better than those in planted forests (R2 ​= ​0.44 for Larix and 0.40 for Quercus, respectively). In both natural and planted forests, the stand age showed a strong relative importance for volume growth (8.6%–66.2%), while the edaphic and climatic variables had a limited relative importance (< 6.0%). The relationship between stand age and volume growth was unimodal in natural forests and linear increase in planted Quercus forests. And the specific locations (i.e., altitude and aspect) of sampling plots exhibited high relative importance for volume growth in planted forests (4.1%–18.2%). Altitude positively affected volume growth in planted Larix forests but controlled volume growth negatively in planted Quercus forests. Similarly, the effects of other environmental factors on volume growth also differed in both stand origins (planted versus natural) and plant functional types (Larix versus Quercus). These results highlighted that the stand age was the most important predictor for volume growth and there were diverse effects of environmental factors on volume growth among stand origins and plant functional types. Our findings will provide a good framework for site-specific recommendations regarding the management practices necessary to maintain the volume growth in China's forest ecosystems.


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About this article

Using machine learning algorithms to estimate stand volume growth of Larix and Quercus forests based on national-scale Forest Inventory data in China

Show Author's information Huiling Tiana, Jianhua Zhua,b,*( ), Xiao Hec, Xinyun Chend, Zunji Jiana, Chenyu Lia, Qiangxin Oue, Qi Lia, Guosheng Huangd, Changfu Liua,b, Wenfa Xiaoa,b,**( )
Ecology and Nature Conservation Institute, Chinese Academy of Forestry, Key Laboratory of Forest Ecology and Environment of National Forestry and Grassland Administration, Beijing, 100091, China
Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, 210037, China
Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Key Laboratory of Forest Management and Growth Modeling, National Forestry and Grassland Administration, Beijing, 100091, China
Academy of Forest Inventory and Planning, National Forestry and Grassland Administration, Beijing, 100714, China
School of Forestry and Landscape Architecture, Anhui Agricultural University, Hefei, 230036, Anhui, China

* Corresponding author. Institute of Forest Ecology, Environment and Nature Conservation, Chinese Academy of Forestry, No. 1 of Dongxiaofu, Xiangshan Road, Haidian District, Beijing, 100091, China.

** Corresponding author. Ecology and Nature Conservation Institute, Chinese Academy of Forestry, Key Laboratory of Forest Ecology and Environment of National Forestry and Grassland Administration, Beijing, 100091, China.

Abstract

Estimating the volume growth of forest ecosystems accurately is important for understanding carbon sequestration and achieving carbon neutrality goals. However, the key environmental factors affecting volume growth differ across various scales and plant functional types. This study was, therefore, conducted to estimate the volume growth of Larix and Quercus forests based on national-scale forestry inventory data in China and its influencing factors using random forest algorithms. The results showed that the model performances of volume growth in natural forests (R2 ​= ​0.65 for Larix and 0.66 for Quercus, respectively) were better than those in planted forests (R2 ​= ​0.44 for Larix and 0.40 for Quercus, respectively). In both natural and planted forests, the stand age showed a strong relative importance for volume growth (8.6%–66.2%), while the edaphic and climatic variables had a limited relative importance (< 6.0%). The relationship between stand age and volume growth was unimodal in natural forests and linear increase in planted Quercus forests. And the specific locations (i.e., altitude and aspect) of sampling plots exhibited high relative importance for volume growth in planted forests (4.1%–18.2%). Altitude positively affected volume growth in planted Larix forests but controlled volume growth negatively in planted Quercus forests. Similarly, the effects of other environmental factors on volume growth also differed in both stand origins (planted versus natural) and plant functional types (Larix versus Quercus). These results highlighted that the stand age was the most important predictor for volume growth and there were diverse effects of environmental factors on volume growth among stand origins and plant functional types. Our findings will provide a good framework for site-specific recommendations regarding the management practices necessary to maintain the volume growth in China's forest ecosystems.

Keywords: Stand volume growth, Stand origin, Plant functional type, National forest inventory data, Random forest algorithms

References(77)

Ahmed, O.S., Franklin, S.E., Wulder, M.A., White, J.C., 2015. Characterizing stand-level forest canopy cover and height using Landsat time series, samples of airborne LiDAR, and the random forest algorithm. ISPRS J. Photogramm. 101, 89-101. https://doi.org/10.1016/j.isprs jprs.2014.11.007.
DOI Google Scholar

Barbaroux, C., Bréda, N., 2002. Contrasting distribution and seasonal dynamics of carbohydrate reserves in stem wood of adult ring-porous sessile oak and diffuse-porous beech trees. Tree Physiol. 22, 1201-1210. https://doi.org/10.1093/treephys/22.17.1201.
DOI Google Scholar

Bergmeir, C., Benítez, J.M., 2012. On the use of cross-validation for time series predictor. Inf. Sci. 191, 192-213.
DOI Google Scholar

Bond-Lamberty, B., Rocha, A.V., Calvin, K., Holmes, B., Wang, C., Goulden, M.L., 2013. Disturbance legacies and climate jointly drive tree growth and mortality in an intensively studied boreal forest. Glob. Chang. Biol. 20(1), 216-227. https://doi.org/10.1111/gcb.12404.
DOI Google Scholar

Breiman, L., 2001. Random forest. Mach. Learn. 45, 5-32.
DOI Google Scholar

Cienciala, E., Russ, R., Santruckova, H., Altman, J., Kopacek, J., Hunova, I., Stepanek, P., Oulehle, F., Tumajer, J., Stahl, G., 2016. Discerning environmental factors affecting current tree growth in Central Europe. Sci. Total Environ. 573, 541-554.
DOI Google Scholar

Crookston, N.L., Rehfeldt, G.E., Dixon, G.E., Weiskittel, A.R., 2010. Addressing climate change in the forest vegetation simulator to assess impacts on landscape forest dynamics. For. Ecol. Manag. 260, 1198-1211. https://doi.org/10.1016/j.foreco.2010.07.013.
DOI Google Scholar

Devi, N., Hagedorn, F., Moiseev, P., Bugmann, H., Shiyatov, S., Mazepa, V., Rigling, A., 2010. Expanding forests and changing growth forms of Siberian larch at the Polar Urals' tree line during the 20th century. Glob. Chang. Biol. 14, 1581-1591. https://doi.org/10.1111/j.1365-2486.2008.01583.x.
DOI Google Scholar

Dulamsuren, C., Hauck, M., Leuschner, C., 2010. Recent drought stress leads to growth reductions in Larix sibirica in the western Khentey, Mongolia. Glob. Chang. Biol. 16, 3024-3035. https://doi.org/10.1111/j.1365-2486.2009.02147.x.
DOI Google Scholar

Forrester, D.I., Baker, T.G., Elms, S.R., Hobi, M.L., Ouyang, S., Wiedemann, J.C., Xiang, W.H., Zell, J., Pulkkinen, M., 2021. Self-thinning tree mortality models that account for vertical stand structure, species mixing and climate. For. Ecol. Manag. 487, 118936.
DOI Google Scholar

Freeman, E.A., Moisen, G.G., Coulston, J.W., Wilson, B.T., 2016. Random forests and stochastic gradient boosting for predicting tree canopy cover: comparing tuning processes and model performance. Can. J. For. Res. 46, 323-339. https://doi.org/10.1139/cjfr-2014-0562.
DOI Google Scholar

Friedman, J.H., 2001. Greedy function approximation: a gradient boosting machine. Ann. Stat. 29, 1189-1232. http://www.jstor.org/stable/2699986.
DOI Google Scholar

Fujimoto, T., Kita, K., Uchiyama, K., Kuromaru, M., Akutsu, H., Oda, K., 2006. Age trends in the genetic parameters of wood density and the relationship with growth rates in hybrid larch (Larix gmelinii var. japonica × L. kaempferi) F1. J. For. Res. 11, 157-163. https://doi.org/10.1007/s10310-005-0200-9.
DOI Google Scholar

Gao, Y., Lu, D., Li, G., Wang, G., Chen, Q., Liu, L., Li, D., 2018. Comparative analysis of modeling algorithms for forest aboveground biomass estimation in a subtropical region. Remote Sens. 10, 627. https://doi.org/10.3390/rs100 40627.
DOI Google Scholar

Glennr, M., 2010. Long-term changes in soils of second-growth forest following abandonment from agriculture. J. Biogeogr. 36, 2066-2075. https://doi.org/10.1111/j.1365-2699.2009.02155.x.
DOI Google Scholar

Goldstein, A., Kapelner, A., Bleich, J., Pitkin, E., 2015. Peeking inside the black box: visualizing statistical learning with plots of individual conditional expectation. J. Comput. Graph. Stat. 24(1), 44-65. https://doi.org/10.1080/10618600.2014.907095.
DOI Google Scholar

Guo, Q., Ren, H., 2014. Productivity as related to diversity and age in planted versus natural forests. Global Ecol. Biogeogr. 23, 1461-1471. https://doi.org/10.1111/geb.12238.
DOI Google Scholar

Guo, Z., Fang, J., Pan, Y., Birdsey, R., 2010. Inventory-based estimates of forest biomass carbon stocks in China: a comparison of three methods. For. Ecol. Manag. 259, 1225-1231. https://doi.org/10.1016/j.foreco.2009.09.047.
DOI Google Scholar

Hasenauer, H., 2006. Sustainable forest management/ Growth models for Europe. Springer-Verlag, Berlin, pp 3-17.
DOI

Hengl, T., de Jesus, J.M., MacMillan, R.A., Batjes, N.H., Heuvelink, G.B.M., Ribeiro, E., Samuel-Rosa, A., Kempen, B., Leenaars, J.G.B., Walsh, M.G., Gonzalez, M.R., 2014. Soilgrids1km - global soil information based on automated mapping. PLoS One 9(8), e105992. https://doi.org/10.1371/journal.pone.0105992.
DOI Google Scholar

Hutchison, K.W., Sherman, C.D., Weber, J., Smith, S.S., Singer, P.B., 1990. Maturation in larch: Ⅱ. Effects of age on photosynthesis and gene expression in developing foliage. Plant Physiol. 94, 1308-1315. https://doi.org/10.1104/pp.94.3.1308.
DOI Google Scholar

Ibanez, B., Ibanez, I., Gómez-Aparicio, L., Ruiz-Benito, P., Garcia, L.V., Maranon, T., 2014. Contrasting effects of climate change along life stages of a dominant tree species: the importance of soil-climate interactions. Divers. Distrib. 20, 872-883.
DOI Google Scholar

Jevsenak, J., Skudnik, M., 2021. A random forest model for basal area increment predictions from National Forest Inventory data. For. Ecol. Manag. 479, 118601. https://doi.org/10.1016/j.foreco.2020.118601.
DOI Google Scholar

Kappelle, M., Geuze, T., Leal, M.E., Cleef, A.M., 1996. Successional age and forest structure in a Costa Rican upper montane Quercus forest. J. Trop. Ecol. 12, 681-698. http://www.jstor.org/stable/2559969.
DOI Google Scholar

Kuhn, M., Johnson, K., 2013. Applied predictive modeling. Springer, New York.
DOI

Lacointe, A., 2000. Carbon allocation among tree organs: a review of basic processes and representation in functional-structural tree models. Ann. For. Sci. 57, 521-533.
DOI Google Scholar

Lebourgeois, F., Cousseau, G., Ducos, Y., 2004. Climate-tree-growth relationship of Quercus petraea Mill. stand in the forest of Berce ("Futaie des Clos", Sarthe, France). Ann. For. Sci. 61, 361-372. https://doi.org/10.1051/forest:2004029.
DOI Google Scholar

Lei, X., Yu, L., Hong, L., 2016. Climate-sensitive integrated stand growth model (CS-ISGM) of Changbai larch (Larix olgensis) plantations. For. Ecol. Manag. 376, 265-275. https://doi.org/10.1016/j.foreco.2016.06.024.
DOI Google Scholar

Lei, X.D., 2019. Applications of machine learning algorithms in forest growth and yield prediction. J. Beijing For. Univ. 41, 23-36 (in Chinese).
Google Scholar

Leng, W.F., He, H.S., Bu, R.C., Hu, Y.M., 2007. Sensitivity analysis of the impacts of climate change on potential distribution of three larch (Larix) species in northeastern China. J. Plant Ecol. 31, 825-833 (in Chinese).
DOI Google Scholar

Levesque, M., Walthert, L., Weber, P., 2016. Soil nutrients influence growth response of temperate tree species to drought. J. Ecol. 104, 377-387.
DOI Google Scholar

Liang, J.J., Zhou, M., Verbyla, D.L., Zhang, L.J., Springsteen, A.L., Malone, T., 2011. Mapping forest dynamics under climate change: a matrix model. For. Ecol. Manag. 262, 2250-2262. https://doi.org/10.1016/j.foreco.2011.08.017.
DOI Google Scholar

Liaw, A., Wiener, M., 2002. Classification and regression by random forests. R. News 2, 18-22. https://doi.org/10.1016/j.foreco.2011.08.017.
DOI Google Scholar

Lu, J., Feng, Z.K., Zhu, Y., 2019. Estimation of forest biomass and carbon storage in China based on forest resources inventory data. Forests 10, 650. https://doi.org/10.3390/f10080650.
DOI Google Scholar

Luo, Y.J., Zhang, X.Q., Wang, X.K., Lu, F., 2016. Biomass and its allocation of Chinese forest ecosystems. Ecology 95(7). https://doi.org/10.1890/13-2089.1.
DOI Google Scholar

Ma, W., Chen, H.C., Wang, X.J., Huang, G.S., 2018. Volume growth rate models of the main natural forest tree species in Tibet. J. Cent. South Univ. For. Tech. 38, 40-45 (in Chinese).
Google Scholar

Ma, W., Sun, Y.J., Guo, X.Y., Ju, W.Z., Mu, J.S., 2010. Carbon storage of Larix olgensis plantation at different stand ages. Acta Ecol. Sin. 30, 4659-4667 (in Chinese).
Google Scholar

Masota, A.M., Zahabu, E., Malimbwi, R.E., 2014. Volume models for single trees in tropical rainforests in Tanzania. J. Energy Nat. Resour. 3, 66-76.
DOI Google Scholar

Matney, T.G., Sullivan, A.D., 1982. Compatible stand and stock tables for thinned and unthinned loblolly pine stands. For. Sci. 28, 161-171. https://doi.org/10.1093/forestscience/28.1.161.
DOI Google Scholar

Moser, L., Fonti, P., Buntgen, U., Esper, J., Luterbacher, J., Franzen, J., 2010. Timing and duration of European larch growing season along altitudinal gradients in the Swiss Alps. Tree Physiol. 30, 225. https://doi.org/10.1093/treephys/tpp108.
DOI Google Scholar

Mura, M., Bottalico, F., Giannetti, F., Bertani, R., Giannini, R., Mancini, M., Orlandini, S., Travaglini, D., Chirici, G., 2018. Exploiting the capabilities of the Sentinel-2 multi spectral instrument for predicting growing stock volume in forest ecosystems. Int. J. Appl. Earth Obs. 66, 126. https://doi.org/10.1016/j.jag.2017.11.013.
DOI Google Scholar

Ndiaye, P., Mailly, D., Pineau, M., Margolis, H.A., 1993. Growth and yield of Casuarina equisetifolia plantations on the coastal sand dunes of Senegal as a function of microtopography. For. Ecol. Manag. 56, 13-28. https://doi.org/10.1016/0378-1127(93)90100-2.
DOI Google Scholar

Ou, Q.X., Lei, X.D., Shen, C.C., 2019. Individual tree diameter growth models of larch-spruce-fir mixed forests based on machine learning algorithms. Forest 10, 187. https://doi.org/10.3390/f10020187.
DOI Google Scholar

Pan, Y., Birdsey, R.A., Fang, J., Houghton, R., Kauppi, P.E., Kurz, W.A., Phillips, O.L., Shvidenko, A., Lewis, S.L., Canadell, J.G., 2011. A large and persistent carbon sinks in the world's forests. Science 333, 988-993. https://doi.org/10.1126/science.1201609.
DOI Google Scholar

Pienaar, L.V., Shiver, B.D., 1984. An analysis and models of basal area growth in 45-year-old unthinned and thinned slash pine plantation plots. For. Sci. 30, 933-942. https://doi.org/10.1093/forestscience/30.4.933.
DOI Google Scholar

Qi, G., Chen, H., Zhou, L., Wang, X.C., Zhou, W.M., Qi, L., Yang, Y.H., Yang, F.L., Wang, Q.L., Dai, L.M., 2015. Carbon stock of larch plantations and its comparison with an old-growth forest in northeast China. Chinese Geogr. Sci. 26, 10-21. https://doi.org/10.1007/s11769-015-0772-z.
DOI Google Scholar

Qi, G., Wang, Q., Wang, X., Lin, Q., Dai, L., 2011. Vegetation carbon storage in Larix gmelinii plantations in Great Xing'an Mountains. Chinese J. Appl. Ecol. 22, 273-279 (in Chinese).
Google Scholar

Qin, J., Cao, Q.V., 2006. Using disaggregation to link individual-tree and whole-stand growth models. Can. J. For. Res. 36, 953-960. https://doi.org/10.1139/X05-284.
DOI Google Scholar

Regos, A., Gagne, L., Alcaraz-Segura, D., Honrado, J.P., Dominguez, J., 2019. Effects of species traits and environmental predictors on performance and transferability of ecological niche models. Sci. Rep. 9, 4221. https://doi.org/10.1038/s41598-019-40766-5.
DOI Google Scholar

Reich, P.B., Luo, Y.J., Bradforde, J.B., Poorterf, H., Perryg, C., Oleksyna, J., 2014. Temperature drives global patterns in forest biomass distribution in leaves, stems, and roots. Proc. Natl. Acad. Sci. Unit. States Am. 111, 13721-13727. https://doi.org/10.1073/pnas.1216053111.
DOI Google Scholar

Robert, A., 2003. Simulation of the effect of topography and tree falls on stand dynamics and stand structure of tropical forests. Ecol. Model. 167, 287-303. https://doi.org/10.1016/s0304-3800(03)00200-x.
DOI Google Scholar

Roberts, D.R., Bahn, V., Ciuti, S., Boyce, M.S., Elith, J., Guillera-Arroita, G., Hauenstein, S., Lahoz-Monfort, J.J., Schroder, B., Thuiller, W., Warton, D.I., Wintle, B.A., Hartig, F., Dormann, C.F., 2017. Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure. Ecography 40, 913-929. https://doi.org/10.1111/ecog.02881.
DOI Google Scholar

Rohner, B., Waldner, P., Lischke, H., Ferretti, M., Thürig, E., 2018. Predicting individual-tree growth of central European tree species as a function of site, stand, management, nutrient, and climate effects. Eur. J. For. Res. 137, 29-44. https://doi.org/10.1007/s10342-017-1087-7.
DOI Google Scholar

Rosenthal, S.I., Camm, E.L., 2010. Effects of air temperature, photoperiod and leaf age on foliar senescence of western larch (Larix occidentalis Nutt. ) in environmentally controlled chambers. Plant Cell Environ. 19, 1057-1065. https://doi.org/10.1111/j.1365-3040.1996.tb00212.x.
DOI Google Scholar

Rozas, V., 2001. Detecting the impact of climate and disturbances on tree-rings of Fagus sylvatica L. and Quercus robur L. in a lowland forest in Cantabria, Northern Spain. Ann. For. Sci. 58, 237-251.
DOI Google Scholar

Sampson, D.A., Wynne, R.H., Seiler, J.R., 2015. Edaphic and climatic effects on forest stand development, net primary production, and net ecosystem productivity simulated for Coastal Plain loblolly pine in Virginia. J. Geophys. Res-Biogeo. 113. https://doi.org/10.1029/2006jg000270.
DOI Google Scholar

Shangguan, W., Dai, Y.J., Liu, B.Y., Zhu, A.X., Duan, Q.Y., Wu, L.Z., Ji, D.Y., Ye, A.Z., Yuan, H., Zhang, Q., Chen, D.D., Chen, M., Chu, J.T., Dou, Y.J., Guo, J.X., Li, H.Q., Li, J.J., Liang, L., Liang, X., Liu, H.P., Liu, S.Y., Miao, C.Y., Zhang, Y.Z., 2013. A China data set of soil properties for land surface modeling. J. Adv. Model. Earth Syst. 5, 212-224. https://doi.org/10.1002/jame.20026.
DOI Google Scholar

Skiadaresis, G., Schwarz, J., Stahl, K., Bauhus, J., 2021. Groundwater extraction reduces tree vitality, growth and xylem hydraulic capacity in Quercus robur during and after drought events. Sci. Rep. 11, 5149. https://doi.org/10.1038/s41598-021-84322-6.
DOI Google Scholar

Slavik, B., 1981. Physiological ecology of the Alpine Timberline. Biol. Plantarum. 23, 334-334. https://doi.org/10.1007/bf02877407.
DOI Google Scholar

Song, Y.H., Chen, D.L., Jia, S.Z., Jian-Cai, G.U., 2016. Effect study on Hylobius albosparsus Boheman by different site conditions in larch plantation. Mod. Agr. Sci. Tech. (in Chinese).
Google Scholar

State Forestry and Grassland Administration of China, 2019. Report of forest resources in China (2014-2018). China Forestry Publishing House, Beijing.

Strobl, C., Boulesteix, A.L., Kneib, T., Augustin, T., Zeileis, A., 2008. Conditional variable importance for random forests. BMC Bioinf. 9, 307. https://doi.org/10.1186/1471-2105-9-307.
DOI Google Scholar

Tian, X.H., Sun, R.X., Li, J., Zhao, C.H., Yang, M.S., 2011. Effects of stand density on growth of Populus × euramericana 'Neva' plantations. Sci. Silvae Sin. 47, 184-188 (in Chinese).
Google Scholar

Wallacy, F.B., Fbio, M.L., Marlon Costa, M., Deivison, V.S., 2014. Equação de volume para apoio ao manejo comunitario de empreendimento florestal em Anapu, Para. Pesquisa Florestal Brasileira, 34, 321-329. https://doi.org/10.4336/2014.pfb.34.80.721.
DOI Google Scholar

Wang, J., Ives, N.E., Lechowicz, M.J., 1992. The relation of foliar phenology to xylem embolism in trees. Funct. Ecol. 6, 469-475.
DOI Google Scholar

Wang, R., Han, R., Long, Q., Gao, X., Zhu, D., 2020. Bacterial and archaeal communities within an ultraoligotrophic, high-altitude lake in the Pre-Himalayas of the Qinghai-Tibet Plateau. Indian J. Microbiol. 60. https://doi.org/10.1007/s12088-020-00881-8.
DOI Google Scholar

Weiskittel, A.R., Crookston, N.L., Radtke, P. J., 2011. Linking climate, gross primary productivity, and site index across forests of the western United States. Can. J. For. Res. 41(8), 1710-1721.
DOI Google Scholar

Xu, Y.N., Zhou, G.C., 1996. Study on the growth rate model of single tree. For. Resour. Manag. 4, 22-24 (in Chinese).
Google Scholar

Yan, W., Duan, G.S., Wang, Y.G., Sun, Z., Zhou, T., Fu, L., 2019. Construction of stand basal area and volume growth model for Quercus and Populus in Henan Province of central China. J. Beijing For. Univ. 41, 55-61 (in Chinese).
Google Scholar

Zhang, C., Denka, S., Cooper, H.M., Mishra, D., 2018. Quantification of sawgrass marsh aboveground biomass in the coastal Everglades using object-based ensemble analysis and Landsat data. Remote Sens. Environ. 204, 366-379. https://doi.org/10.1016/j.rse.2017.10.018.
DOI Google Scholar

Zhang, L., Wang, L.L., Zhang, X.D., Liu, S.R., Sun, P.S., Wang, T.L., 2014. The basic principle of random forest and its applications in ecology: a case study of Pinus yunnanensis. Acta Ecol. Sin. 34(3), 650-659. https://doi.org/10.5846/stxb201306031292.
DOI Google Scholar

Zhang, R., Pang, Y., Li, Z., Bao, Y., 2016. Canopy closure estimation in a temperate forest using airborne LiDAR and LANDSAT ETM+ data. Chinese J. Plant Ecol. 40, 102-115 (in Chinese).
DOI Google Scholar

Zhang, X., Lei, Y., Cao, Q.V., 2010. Compatibility of stand basal area predictions based on forecast combination. For. Sci. 56, 552-557. https://doi.org/10.1093/forestscience/56.6.552.
DOI Google Scholar

Zhang, X.Q., Xu, D.Y., 2003. Potential carbon sequestration in China's forests. Environ. Sci. Policy 6(5), 421-432. https://doi.org/10.1016/s1462-9011(03)00072-8.
DOI Google Scholar

Zhao, K.J., Wang, L.D., Wang, L.J., Jia, Z.K., Ma, L.Y., 2015. Stock volume and productivity of Larix principis-rupprechtii in northern and northwestern China. J. Beijing For. Univ. 37, 24-31 (in Chinese).
Google Scholar

Zheng, D., van der Velde, R., Su, Z., Wang, X., Wen, J., Booij, M.J., Hoekstra, A.Y., Chen, Y., 2015. Augmentations to the Noah Model Physics for application to the Yellow River source area. Part Ⅰ: soil water flow. J. Hydrometeorol. 16(6), 2659-2676. https://doi.org/10.1175/jhm-d-14-0198.1.
DOI Google Scholar

Zhou, Z.H., 2016. Machine learning. Tsinghua University Press, Beijing.
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Received: 21 January 2022
Revised: 13 March 2022
Accepted: 20 March 2022
Published: 21 April 2022
Issue date: June 2022

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Acknowledgements

Acknowledgements

This work was supported by the Major Program of the National Natural Science Foundation of China(No. 32192434), the Fundamental Research Funds of Chinese Academy of Forestry (No. CAFYBB2019ZD001) and the National Key Research and Development Program of China (2016YFD060020602). We thank our colleagues, Yanyan Ni, Yu Tian, for their assistance in providing relevant factor data and data sorting. We thank Shuyi Guo, Rui Wang, staff of the Academy of Forest Inventory and Planning, National Forestry and Grassland Administration, for help with data provision. We also thank the Zigui Forest Ecosystem Research Station for their help.

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