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Lou Minghua, Zhang Huiru, Lei Xiangdong, Bai Chao, Yang Tonghui. Relationship model between stand mean height and mean DBH for natural Quercus spp. broadleaved mixed stands[J]. Journal of Beijing Forestry University, 2020, 42(9): 37-50. DOI: 10.12171/j.1000-1522.20190463
Citation: Lou Minghua, Zhang Huiru, Lei Xiangdong, Bai Chao, Yang Tonghui. Relationship model between stand mean height and mean DBH for natural Quercus spp. broadleaved mixed stands[J]. Journal of Beijing Forestry University, 2020, 42(9): 37-50. DOI: 10.12171/j.1000-1522.20190463

Relationship model between stand mean height and mean DBH for natural Quercus spp. broadleaved mixed stands

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  • Received Date: December 10, 2019
  • Revised Date: January 01, 2020
  • Available Online: September 16, 2020
  • Published Date: September 29, 2020
  •   Objective  Considering stand density, diameter structure and tree species structure, the optimal model for stand mean height and mean DBH relationship was constructed using algebraic difference approach. It may provide a theoretical basis for site productivity estimation and sustainable management of natural mixed forests.
      Method  Base algebraic difference approaches were modeled with 4 different data structure types, i.e. typeC, typeD, typeE and typeF based on Richards model using 4 inventory data of permanent sample plots in natural Quercus spp. broadleaved mixed stands. The 4 different base algebraic difference approaches were comparatively analyzed to get the optimal data structure type. Algebraic difference approach of diversity indices was constructed based on the optimal data structure type using 5 different stand density indices, including tree number (N), stand basal area (BA), stand density index (SDIr), additive stand density index (SDIa) and canopy density (CD), and the 5 different diameter diversity indices including Shannon evenness index (ShaI), Simpson index (SimI), McIntosh evenness index (MceI), Gini coefficient (GinI) and Berger-Parker index (BerI), and the 4 different species diversity indices including ShaI, SimI, MceI and BerI. The algebraic difference approach of diversity indices was comparatively analyzed to obtain the optimize algebraic difference approaches, i.e. the optimize stand mean height and mean DBH relationship.
      Result  Model fitting effects of calibration data in different data structure types were sorted from best to worst, and the ranking was: typeD > typeC > typeF > typeE. Except for typeC, model coefficients b and r of the other three data structure types were significant (P < 0.01), indicating that the model fitting effects of typeD were the best. Model fitting effects of SDIr were the best. Model coefficients b0, r and cSD were significant (P < 0.01), regardless of which stand density index was used, indicating that model fitting effects of the 5 different stand density indices were reasonable. Model fitting effect of ShaI was the best. Except for GinI, model coefficients b0, r, cSDIr and cDI of the other 4 diameter diversity indices were significant (P < 0.01), indicating that model fitting effects of ShaI, SimI, MceI and BerI were reasonable. Model fitting and validation effects had little difference among the 4 species diversity indices. Model coefficients b0, r, cSDIr, cShaI and cSP of BerI were significant (P < 0.01), indicating that BerI was reasonable. However, model coefficients b0, r, cSDIr, cShaI and cSP of ShaI, SimI and MceI were not significant at the level of 0.05, indicating that ShaI, SimI and MceI were not reasonable.
      Conclusion  TypeD is the best data structure type, stand density, diameter diversity and species diversity were significant for algebraic difference approach. Moreover, the model fitting effects of algebraic difference approach within SDIr, ShaI and BerI are the best, which is served as the optimize stand mean height and mean DBH relationship in natural Quercus spp. broadleaved mixed stands.
  • [1]
    Skovsgaard J P, Vanclay J K. Forest site productivity: a review of the evolution of dendrometric concepts for even-aged stands[J]. Forestry: An International Journal of Forest Research, 2008, 81(1): 13−31. doi: 10.1093/forestry/cpm041
    [2]
    Skovsgaard J P, Vanclay J K. Forest site productivity: a review of spatial and temporal variability in natural site conditions[J]. Forestry: An International Journal of Forest Research, 2013, 86(3): 305−315. doi: 10.1093/forestry/cpt010
    [3]
    Hägglund B. Evaluation of forest site productivity[J]. Forestry Abstracts, 1981, 42(11): 516−527.
    [4]
    Rennolls K. “Top height”: its definition and estimation[J]. Commonwealth Forestry Review, 1978, 57(3): 215−219.
    [5]
    Huang S M, Titus S J. An index of site productivity for uneven-aged or mixed-species stands[J]. Canadian Journal of Forest Research, 1993, 23(3): 558−562. doi: 10.1139/x93-074
    [6]
    Palahí M, Pukkala T, Kasimiadis D, et al. Modelling site quality and individual-tree growth in pure and mixed Pinus brutia stands in northeast Greece[J]. Annals of Forest Science, 2008, 65(5): 501. doi: 10.1051/forest:2008022
    [7]
    Nigh G D. The geometric mean regression line: a method for developing site index conversion equations for species in mixed stands[J]. Forest Science, 1995, 41(1): 84−98. doi: 10.1093/forestscience/41.1.84
    [8]
    Johansson T. Site index conversion equations for Picea abies and five broadleaved species in Sweden: Alnus glutinosa, Alnus incana, Betula pendula, Betula pubescens and Populus tremula[J]. Scandinavian Journal of Forest Research, 2006, 21(1): 14−19. doi: 10.1080/02827580500526015
    [9]
    Raulier F, Lambert M C, Pothier D, et al. Impact of dominant tree dynamics on site index curves[J]. Forest Ecology and Management, 2003, 184(1/3): 65−78.
    [10]
    Ouzennou H, Pothier D, Raulier F. Adjustment of the age-height relationship for uneven-aged black spruce stands[J]. Canadian Journal of Forest Research, 2008, 38(7): 2003−2012. doi: 10.1139/X08-044
    [11]
    Anyomi K A, Raulier F, Bergeron Y, et al. Spatial and temporal heterogeneity of forest site productivity drivers: a case study within the eastern boreal forests of Canada[J]. Landscape Ecology, 2014, 29(5): 905−918. doi: 10.1007/s10980-014-0026-y
    [12]
    McCarthy J W, Weetman G. Stand structure and development of an insect-mediated boreal forest landscape[J]. Forest Ecology and Management, 2007, 241(1/3): 101−114.
    [13]
    Boucher D, Gauthier S, De Grandpré L. Structural changes in coniferous stands along a chronosequence and a productivity gradient in the northeastern boreal forest of Québec[J]. Écoscience, 2006, 13(2): 172−180. doi: 10.2980/i1195-6860-13-2-172.1
    [14]
    Vanclay J K. Site productivity assessment in rainforests: an objective approach using indicator species[M]//Mohd W R, Chan H T, Appanah S. Seminar on growth and yield in tropical mixed/moist forests. Kuala Lumpur: Forest Research Institute, 1989: 225−241.
    [15]
    Meyer H A. A mathematical expression for height curves[J]. Journal of Forestry, 1940, 38(5): 415−420.
    [16]
    Husch B, Miller C I, Beers T W. Forest mensuration [M]. New York: John Wiley & Sons, 1982.
    [17]
    Lei X D, Tang M P, Lu Y C, et al. Forest inventory in China: status and challenges[J]. International Forestry Review, 2009, 11(1): 52−63. doi: 10.1505/ifor.11.1.52
    [18]
    Zeng W S, Tomppo E, Healey S P, et al. The national forest inventory in China: history-results-international context[J]. Forest Ecosystems, 2015, 2(1): 1−16. doi: 10.1186/s40663-014-0025-0
    [19]
    Pienaar L V, Shiver B D. An analysis and models of basal area growth in 45-year-old unthinned and thinned slash pine plantation plots[J]. Forest Science, 1984, 30(4): 933−942.
    [20]
    Lanner R M. On the insensitivity of height growth to spacing[J]. Forest Ecology and Management, 1985, 13(3/4): 143−148.
    [21]
    Bontemps J D, Bouriaud O. Predictive approaches to forest site productivity: recent trends, challenges and future perspectives[J]. Forestry: An International Journal of Forest Research, 2014, 87(1): 109−128. doi: 10.1093/forestry/cpt034
    [22]
    傅立国, 陈谭清, 郎楷永, 等. 中国高等植物[M]. 青岛: 青岛出版社, 2001: 240−254.

    Fu L G, Chen T Q, Lang K Y, et al. Higher plants of China[M]. Qingdao: Qingdao Publishing House, 2001: 240−254.
    [23]
    国家林业局. 第八次全国森林资源清查结果[J]. 林业资源管理, 2014(1):1−2.

    State Forestry Bureau. The 8th national forest inventory[J]. Forest Resources Management, 2014(1): 1−2.
    [24]
    郭斌. 栎属近缘种指纹图谱构建及遗传结构[J]. 北京林业大学学报, 2018, 40(5):10−18.

    Guo B. Construction of SSR fingerprint and research of genetic structure in relative Quercus species[J]. Journal of Beijing Forestry University, 2018, 40(5): 10−18.
    [25]
    官秀玲, 胡艳波. 我国栎类经营及其发展方向研究[J]. 西部林业科学, 2019, 48(2):146−150, 158.

    Guan X L, Hu Y B. Research on oak forest management orientation of China[J]. Journal of West China Forestry Science, 2019, 48(2): 146−150, 158.
    [26]
    盛炜彤. 我国应将天然次生林的经营放在重要位置[J]. 林业科技通讯, 2016(2):10−13.

    Sheng W T. China should put an important position for the management of natural secondary forests[J]. Forest Science and Technology, 2016(2): 10−13.
    [27]
    张晓红, 张会儒. 蒙古栎次生林垂直结构特征对目标树经营的响应[J]. 北京林业大学学报, 2019, 41(5):56−65.

    Zhang X H, Zhang H R. Response of vertical structure characteristics of natural secondary Quercus mongolica forest to crop tree release[J]. Journal of Beijing Forestry University, 2019, 41(5): 56−65.
    [28]
    Reineke L H. Perfecting a stand-density index for even-aged forests[J]. Journal of Agricultural Research, 1933, 46(7): 627−638.
    [29]
    Stage A R. A tree-by-tree measure of site utilization for grand fir related to stand density index[R]. Washington: U.S. Forest Service Research, 1968.
    [30]
    Long J N, Daniel T W. Assessment of growing stock in uneven-aged stands[J]. Western Journal of Applied Forestry, 1990, 5(3): 93−96. doi: 10.1093/wjaf/5.3.93
    [31]
    张连金, 惠刚盈, 孙长忠. 不同林分密度指标的比较研究[J]. 福建林学院学报, 2011, 31(3):257−261.

    Zhang L J, Hui G Y, Sun C Z. Comparison of different stand density measures[J]. Journal of Fujian College of Forestry, 2011, 31(3): 257−261.
    [32]
    Lexerød N L, Eid T. An evaluation of different diameter diversity indices based on criteria related to forest management planning[J]. Forest Ecology and Management, 2006, 222(1/3): 17−28.
    [33]
    Wang M L, Borders B E, Zhao D H. Parameter estimation of base-age invariant site index models: which data structure to use?[J]. Forest Science, 2007, 53(5): 541−551.
    [34]
    倪成才, 于福平, 张玉学, 等. 差分生长模型的应用分析与研究进展[J]. 北京林业大学学报, 2010, 32(4):284−292.

    Ni C C, Yu F P, Zhang Y X, et al. Application analysis and recent advances of projection growth models[J]. Journal of Beijing Forestry University, 2010, 32(4): 284−292.
    [35]
    Getis A, Aldstadt J. Constructing the spatial weights matrix using a local statistic[J]. Geographical Analysis, 2004, 36(2): 90−104. doi: 10.1111/j.1538-4632.2004.tb01127.x
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