Scientia Silvae Sinicae ›› 2021, Vol. 57 ›› Issue (9): 87-97.doi: 10.11707/j.1001-7488.20210909
Previous Articles Next Articles
Lele Lu1,2,Zhen Wang1,Xiongqing Zhang1,2,*,Jianguo Zhang1
Received:
2020-06-24
Online:
2021-09-25
Published:
2021-11-29
Contact:
Xiongqing Zhang
CLC Number:
Lele Lu,Zhen Wang,Xiongqing Zhang,Jianguo Zhang. Individual Tree Diameter Growth Model of Chinese Fir Plantations Using Bayesian Model Averaging and Stepwise Regression Approaches[J]. Scientia Silvae Sinicae, 2021, 57(9): 87-97.
Table 1
Summary statistics of internal factors"
初植密度Planting density | 胸高断面积Basal area (BA)/(m2·hm-2) | 林分密度Number (N)/(trees·hm-2) | 优势木平均高Dominant height (Hd)/(m) | 大于对象木的断面积和Sum of basal areas of trees larger than the subject tree (BAL)/(m2·hm-2) | 胸径Diameter at breast height (DBH)/(cm) | |||||||||
均值Mean | 标准差SD | 均值Mean | 标准差SD | 均值Mean | 标准差SD | 均值Mean | 标准差SD | 均值Mean | 标准差SD | |||||
A | 31.57 | 19.66 | 1 630 | 107.22 | 13.71 | 6.44 | 19.13 | 16.81 | 14.38 | 6.67 | ||||
B | 37.01 | 21.27 | 3 190 | 325.67 | 12.52 | 5.79 | 22.96 | 18.70 | 11.27 | 5.22 | ||||
C | 38.75 | 22.24 | 4 635 | 685.83 | 12.35 | 5.96 | 24.86 | 19.70 | 9.64 | 4.87 | ||||
D | 41.19 | 21.15 | 6 084 | 966.38 | 11.52 | 5.85 | 27.01 | 21.86 | 8.95 | 4.75 | ||||
E | 39.50 | 20.35 | 8 739 | 2 073.10 | 10.65 | 5.11 | 25.96 | 18.65 | 7.53 | 4.12 |
Table 2
Summary statistics of climate factors"
气候变量Climate variable | 含义Description | 最小值Min | 最大值Max | 平均值Mean | 标准差SD |
MAT/℃ | 年均气温Mean annual temperature | 18.10 | 19.80 | 18.96 | 0.45 |
MWMT/℃ | 最热月平均温度Mean warmest month temperature | 26.50 | 30.30 | 28.26 | 0.94 |
MCMT/℃ | 最冷月平均温度Mean coldest month temperature | 5.20 | 10.20 | 8.34 | 1.17 |
AP/mm | 年均降雨量Mean annual precipitation | 1 390.00 | 2 416.00 | 1 795.79 | 271.39 |
AHM | 年均干旱指数Annual heat-moisture index | 11.90 | 21.40 | 16.45 | 2.35 |
DD0/d | 低于0℃天数Degree-days below 0 ℃ | 1.00 | 3.00 | 1.48 | 0.63 |
SMMT/℃ | 夏季平均最高温度Summer mean maximum temperature | 30.30 | 33.80 | 32.10 | 0.81 |
WMMT/℃ | 冬季平均最低温度Winter mean minimum temperature | 2.50 | 6.60 | 4.95 | 0.96 |
SMT/℃ | 春季平均气温Spring mean temperature | 16.90 | 19.60 | 18.53 | 0.75 |
Table 3
Individual tree diameter growth model by BMA and SR methods and posterior probability of BMA model"
初植密度Planting density | BMA | SR | 2种方法的模型是否相似SR model = BMA model or not | ||
后验概率Posterior probability | 选择的模型变量Selected model variables | 选择的模型变量Selected model variables | |||
A | 模型1 Model 1 (0.656) | N, Dg, DBH, lnDBH, lnA, MWMT, MCMT, AP, AHM, SMMT, WMMT | N, BA, DBH, lnDBH, lnA, MWMT, MCMT, AP, AHM, DD0, SMMT, WMMT, SMT | 模型2 Model 2 | |
模型2 Model 2(0.069) | N, BA, DBH, lnDBH, lnA, MWMT, MCMT, AP, AHM, DD0, SMMT, WMMT, SMT | ||||
模型3 Model 3(0.062) | N, Dg, DBH, lnDBH, lnA, MAT, MWMT, MCMT, AP, DD0, SMMT, WMMT | ||||
模型4 Model 4(0.062) | N, Dg, DBH, lnDBH, lnA, MWMT, MCMT, AP, AHM, DD0, SMMT, WMMT | ||||
模型5 Model 5(0.043) | N, Dg, DBH, lnDBH, lnA, MWMT, MCMT, AP, AHM, DD0, SMMT, WMMT, SMT | ||||
B | 模型1 Model 1(0.743) | N, Dg, Hd, DBH, lnDBH, lnA, BAL, MWMT, MCMT, AP, SMMT, WMMT | N, Dg, Hd, DBH, lnDBH, lnA, BAL, MWMT, MCMT, AP, DD0, SMMT, WMMT, SMT | 否No | |
模型2 Model 2 (0.257) | N, Dg, Hd, DBH, lnDBH, lnA, BAL, MWMT, MCMT, AP, WMMT | ||||
C | 模型1 Model 1 (0.447) | N, BA, Dg, DBH, lnDBH, lnA, BAL, MWMT, MCMT, AP, DD0, SMMT, WMMT | N, BA, Dg, Hd, DBH, lnDBH, lnA, BAL, MAT, MWMT, MCMT, AP, AHM, DD0, SMMT, WMMT, SMT | 否No | |
模型2 Model 2 (0.228) | N, BA, Dg, Hd, DBH, lnDBH, lnA, BAL, MWMT, MCMT, AP, DD0, SMMT, WMMT | ||||
模型3 Model 3 (0.155) | N, BA, Dg, DBH, lnDBH, lnA, BAL, MCMT, AP, DD0, WMMT | ||||
模型4 Model 4 (0.095) | N, BA, Dg, DBH, lnDBH, lnA, BAL, MWMT, MCMT, AP, SMMT, WMMT | ||||
模型5 Model 5 (0.038) | N, BA, Dg, DBH, lnDBH, lnA, BAL, MWMT, MCMT, AP, WMMT | ||||
D | 模型1 Model 1(0.825) | N, BA, Dg, Hd, DBH, lnA, BAL, MAT, AHM, DD0, WMMT, SMT | N, BA, Dg, Hd, DBH, lnA, BAL, MAT, MWMT, AP, AHM, DD0, SMMT, WMMT, SMT | 模型2 Model 2 | |
模型2 Model 2(0.069) | N, BA, Dg, Hd, DBH, lnA, BAL, MAT, MWMT, AP, AHM, DD0, SMMT, WMMT, SMT | ||||
模型3 Model 3 (0.062) | N, BA, Dg, Hd, DBH, lnA, BAL, MAT, AP, DD0, WMMT, SMT | ||||
模型4 Model 4 (0.043) | N, BA, Dg, Hd, DBH, lnA, BAL, MAT, AHM, WMMT, SMT | ||||
E | 模型1 Model 1(0.594) | BA, Dg, DBH, lnDBH, lnA, BAL, MAT, MWMT, MCMT, AHM, DD0, SMMT, WMMT, SMT | BA, Dg, DBH, lnDBH, lnA, BAL, MAT, MWMT, MCMT, AHM, DD0, SMMT, WMMT, SMT | 模型1 Model 1 | |
模型2 Model 2 (0.406) | BA, Dg, DBH, lnDBH, lnA, BAL, MAT, MWMT, MCMT, AP, DD0, SMMT, WMMT, SMT | ||||
全样本数据All data | 模型1 Model 1(0.679) | N, BA, Dg, Hd, DBH, lnA, BAL, MAT, MWMT, MCMT, AP, AHM, DD0, WMMT, SMT | N, BA, Dg, Hd, DBH, lnDBH, lnA, BAL, MAT, MWMT, MCMT, AP, AHM, DD0, WMMT, SMT | 否No | |
模型2 Model 2(0.321) | N, BA, Dg, Hd, DBH, lnA, BAL, MAT, MWMT, MCMT, AP, DD0, WMMT, SMT |
Table 4
Evaluation of individual tree diameter growth model based on BMA and SR methods"
初植密度Planting density | R2 | MAD | |||
BMA | SR | BMA | SR | ||
A | 0.793 4 | 0.793 7 | 0.227 4 | 0.226 6 | |
B | 0.732 4 | 0.733 1 | 0.204 3 | 0.203 2 | |
C | 0.714 3 | 0.714 5 | 0.194 5 | 0.193 9 | |
D | 0.676 7 | 0.688 5 | 0.191 2 | 0.190 8 | |
E | 0.340 0 | 0.340 1 | 0.235 0 | 0.235 5 | |
全样本数据All data | 0.612 1 | 0.611 8 | 0.220 4 | 0.220 4 |
Table 5
The parameter estimates of individual tree diameter growth model by BMA and SR methods"
变量Variable | A | B | C | D | E | 全样本数据All data | |||||||||||
SR | BMA | SR | BMA | SR | BMA | SR | BMA | SR | BMA | SR | BMA | ||||||
N | 0.000 9* | -0.000 9 (1.00) | 0.000 2* | 0.000 2 (1.00) | 0.000 2* | 0.000 2 (1.00) | 0.000 2* | 0.000 1 (1.00) | — | — | 0.000 03* | 0.000 03 (1.00) | |||||
BA | 0.004 1* | — | — | — | 0.005 5* | 0.005 1 (1.00) | 0.004 6* | 0.004 7 (1.00) | 0.003 7* | 0.003 7 (1.00) | 0.001 8* | 0.002 0 (1.00) | |||||
Dg | — | -0.016 5 (0.89) | 0.056 5* | 0.063 4 (1.00) | 0.069 8* | 0.086 9 (1.00) | 0.090 5* | 0.086 0 (1.00) | 0.024 6* | 0.024 2 (1.00) | -0.027 5* | -0.027 0 (1.00) | |||||
Hd | — | — | 0.014 3* | 0.016 6 (1.00) | 0.008 5* | — | 0.014 2* | 0.014 0 (1.00) | — | — | 0.005 1* | 0.005 1 (1.00) | |||||
DBH | 0.034 2* | 0.034 0 (1.00) | 0.022 9* | 0.024 3 (1.00) | 0.012 0* | 0.012 9 (1.00) | 0.017 9* | 0.017 0 (1.00) | 0.014 9* | 0.014 9 (1.00) | 0.016 3* | 0.015 0 (1.00) | |||||
lnDBH | 0.153 2* | -0.151 8 (1.00) | 0.096 3* | 0.103 0 (1.00) | 0.064 9* | 0.061 6 (1.00) | — | — | 0.088 96* | 0.088 2 (1.00) | -0.011 4* | — | |||||
lnA | 0.727 6* | -0.735 8 (1.00) | 0.262 0* | 0.228 2 (1.00) | 0.195 2* | 0.172 8 (1.00) | 0.320 5* | 0.280 0 (1.00) | -0.405 8* | 0.397 9 (1.00) | -0.291 0* | -0.300 0 (1.00) | |||||
BAL | — | — | 0.003 3* | 0.003 1 (1.00) | 0.003 8* | 0.003 6 (1.00) | 0.004 5* | 0.004 7 (1.00) | -0.006 1* | 0.006 1 (1.00) | -0.004 2* | -0.004 3 (1.00) | |||||
MAT | 0.139 3 | — | — | — | 0.208 4* | — | 0.264 9* | 0.100 0 (1.00) | -0.149 1* | 0.195 0 (1.00) | -0.198 5* | -0.180 0 (1.00) | |||||
MWMT | 0.128 1* | 0.129 4 (1.00) | 0.071 7* | 0.048 9 (1.00) | 0.053 9* | 0.037 9 (0.81) | 0.038 7* | — | 0.091 8* | 0.090 2 (1.00) | 0.028 7* | 0.027 0 (1.00) | |||||
MCMT | 0.195 3* | 0.178 0 (1.00) | 0.098 4* | 0.083 5 (1.00) | 0.076 6* | 0.049 1 (1.00) | — | — | 0.078 6* | 0.078 5 (1.00) | 0.059 0* | 0.056 0 (1.00) | |||||
AP | 0.001 0* | 0.000 7 (1.00) | 0.000 2* | 0.000 2 (1.00) | 0.000 6* | 0.000 2 (1.00) | 0.000 4* | — | — | — | 0.000 4* | 0.000 3 (1.00) | |||||
AHM | 0.078 2* | 0.051 4 (0.87) | — | — | 0.051 2* | — | 0.056 6* | 0.015 0 (0.94) | -0.032 5* | — | 0.002 3* | — | |||||
DD0 | 0.869 3* | — | 0.037 4* | — | 0.082 0* | 0.025 6 (0.83) | 0.054 2* | 0.041 0 (0.96) | 0.111 8* | 0.116 9 (1.00) | 0.081 3* | 0.082 0 (1.00) | |||||
SMMT | 0.206 8* | -0.227 6 (1.00) | 0.098 2* | 0.052 8 (0.74) | 0.059 8* | 0.059 3 (0.77) | 0.089 3* | — | -0.107 3* | 0.097 9 (1.00) | — | — | |||||
WMMT | 0.156 3* | -0.172 6 (1.00) | 0.106 6* | 0.104 5 (1.00) | 0.052 0* | 0.067 9 (1.00) | 0.063 3* | 0.041 0 (1.00) | -0.070 4* | 0.066 6 (1.00) | -0.050 3* | -0.051 0 (1.00) | |||||
SMT | 0.069 8* | — | 0.016 7* | — | 0.065 9* | — | 0.084 6* | 0.045 0 (1.00) | 0.112 6* | 0.123 7 (1.00) | 0.090 4* | 0.088 0 (1.00) |
程瑞梅, 刘泽彬, 封晓辉, 等. 气候变化对树木木质部生长影响的研究进展. 林业科学, 2015, 51 (6): 147- 154. | |
Cheng R M , Liu Z B , Feng X H , et al. Advances in research on the effect of climatic change on xylem growth of trees. Scientia Silvae Sinicae, 2015, 51 (6): 147- 154. | |
姜倩倩, 田娜娜, 夏泰英, 等. 温度、降水与树木径向生长关系研究进展. 山东农业大学学报: 自然科学版, 2012, 43 (3): 480- 482.
doi: 10.3969/j.issn.1000-2324.2012.03.030 |
|
Jiang Q Q , Tian N N , Xia T Y , et al. Research progress on the relationship between temperature, precipitation and radial growth of trees. Journal of Shandong Agricultural University: Natural Science Edition, 2012, 43 (3): 480- 482.
doi: 10.3969/j.issn.1000-2324.2012.03.030 |
|
黎敬业, 黄建国, 梁寒雪, 等. 中国东南部不同海拔亚热带森林中马尾松径向生长对气候的响应. 热带亚热带植物学报, 2019, 27 (6): 633- 641. | |
Li J Y , Huang J G , Liang H X , et al. Response of radial growth of Masson pine to climate in subtropical forests at different elevations in southeast China. Journal of Tropical and Subtropical Botany, 2019, 27 (6): 633- 641. | |
李春明. 基于两层次线性混合效应模型的杉木林单木胸径生长量模型. 林业科学, 2012, 48 (3): 66- 73. | |
Li C M . Individual tree diameter increment model for Chinese fir plantation based on two-level linear mixed effects models. Scientia Silvae Sinicae, 2012, 48 (3): 66- 73. | |
李文馨, 刘世波. 包括气候变量的大尺度柏木胸径单木生长模型. 中南林业科技大学学报, 2015, 35 (3): 74- 77. | |
Li W X , Liu S B . Large scaled cedar DBH growth models including climate variables. Journal of Central South University of Forestry & Technology, 2015, 35 (3): 74- 77. | |
罗剑锋. 2003. 贝叶斯平均模型及其在医学研究中的应用探索. 上海: 复旦大学硕士学位论文. | |
Luo J F. 2003. Bayesian model average and its application on variables selection in medical research. Shanghai: MS thesis of Fudan University. [in Chinese] | |
欧强新, 雷相东, 沈琛琛, 等. 基于随机森林算法的落叶松-云冷杉混交林单木胸径生长预测. 北京林业大学学报, 2019, 41 (9): 9- 19. | |
Ou Q X , Lei X D , Shen C C , et al. Individual tree DBH growth prediction of larch-spruce-fir mixed forests based on random forest algorithm. Journal of Beijing Forestry University, 2019, 41 (9): 9- 19. | |
王冬至, 张宏卓, 张冬燕, 等. 塞罕坝华北落叶松-白桦针阔混交林胸径年生长量预测. 西北林学院学报, 2017, 32 (3): 1- 6.
doi: 10.3969/j.issn.1001-7461.2017.03.01 |
|
Wang D Z , Zhang H Z , Zhang D Y , et al. Prediction of the diameter annual radial growth of Larix principis-rupprechtii and Betula platyphylla mixed forest in Saihanba. Journal of Northwest Forestry University, 2017, 32 (3): 1- 6.
doi: 10.3969/j.issn.1001-7461.2017.03.01 |
|
王延芳, 张永香, 勾晓华, 等. 祁连山中部低海拔地区青海云杉径向生长的气候响应机制. 生态学报, 2020, 40 (1): 161- 169. | |
Wang Y F , Zhang Y X , Gou X H , et al. Climate response mechanism of radial growth of Picea crassifolia in low altitude area of middle Qilian Mountains. Acta Ecologica Sinica, 2020, 40 (1): 161- 169. | |
余黎, 雷相东, 王雅志, 等. 基于广义可加模型的气候对单木胸径生长的影响研究. 北京林业大学学报, 2014, 36 (5): 22- 32. | |
Yu L , Lei X D , Wang Y Z , et al. Impact of climate on individual tree radial growth based on generalized additive model. Journal of Beijing Forestry University, 2014, 36 (5): 22- 32. | |
张海平. 2017. 基于气象因子的天然白桦林单木胸径生长模型的研究. 哈尔滨: 东北林业大学硕士学位论文. | |
Zhang H P. 2017. Individual tree diameter increment model for natural Betula platyphylla forests based on meteorological factors. Harbin: MS thesis of Northeast Forestry University. [in Chinese] | |
张志杰, 彭文祥, 周艺彪, 等. 贝叶斯模型平均法的基本原理及其在Logistic回归中的应用实例. 中国卫生统计, 2007, 24 (5): 467- 471.
doi: 10.3969/j.issn.1002-3674.2007.05.006 |
|
Zhang Z J , Peng W X , Zhou Y B , et al. The basic principle of Bayesian model averaging method and its application in Logistic regression. Chinese Journal of Health Statistics, 2007, 24 (5): 467- 471.
doi: 10.3969/j.issn.1002-3674.2007.05.006 |
|
Allen C D , Breshears D D , McDowell N G . On underestimation of global vulnerability to tree mortality and forest die-off from hotter drought in the Anthropocene. Ecosphere, 2015, 6 (8): 1- 55. | |
Aragao L E O C , Malh Y , Metcalfe D B , et al. Above- and below-ground net primary productivity across ten Amazonian forests on contrasting soils. Biogeosciences, 2009, 6, 2759- 2778.
doi: 10.5194/bg-6-2759-2009 |
|
Baskerville G L . Use of logarithmic regression in the estimation of plant biomass. Canadian Journal of Forest Research, 1972, 2 (1): 49- 53.
doi: 10.1139/x72-009 |
|
Box G E P, Tiao G C. 1973. Bayesian inference in statistical analysis. Addison-Wesley, Reading, MA. | |
Brooks J R , Flanagan L , Ehleringer J . Responses of boreal conifers to climate fluctuations: indications from tree-ring widths and carbon isotope analyses. Canadian Journal of Forest Research, 1998, 28, 524- 533.
doi: 10.1139/x98-018 |
|
Bullock B P , Boone E L . Deriving tree diameter distributions using Bayesian model averaging. Forest Ecology and Management, 2007, 242, 127- 132.
doi: 10.1016/j.foreco.2007.01.024 |
|
Calama R , Montero G . Multilevel linear mixed model for tree diameter increment in stone pine (Pinus pinea): a calibrating approach. Silva Fennica, 2005, 39 (1): 37- 54. | |
Clark J S , Bell D M , Kwit M C , et al. Competition-interaction landscapes for the joint response of forests to climate change. Global Change Biology, 2014, 20 (6): 1979- 1991.
doi: 10.1111/gcb.12425 |
|
DeRose R J , Seymour R S . The effect of site quality on growth efficiency of upper crown class Picea rubens and Abies balsamea in Maine, USA. Canadian Journal of Forest Research, 2009, 39 (4): 777- 784.
doi: 10.1139/X09-012 |
|
DraperD . Assessment and propagation of model uncertainty. Journal of the Royal Statistical Society: Series B (Methodological, 1995, 57 (1): 45- 70.
doi: 10.1111/j.2517-6161.1995.tb02015.x |
|
Foster J R , D'Amato A W . Looking for age-related growth decline in natural forests: unexpected biomass patterns from tree rings and simulated mortality. Oecologia, 2014, 175, 363- 374.
doi: 10.1007/s00442-014-2881-2 |
|
Genell A , Nemes S , Steinec G , et al. Model selection in medical research: a simulation study comparing Bayesian model averaging and stepwise regression. BMC Medical Research Methodology, 2010, 10 (1): 108.
doi: 10.1186/1471-2288-10-108 |
|
Hanewinkel M , Cullmann D A , Schelhaas M J , et al. Climate change may cause severe loss in the economic value of European forest land. Nature Climate Change, 2012, 3, 203- 207. | |
Henderson J P, Grissino-Mayer H D. 2009. Climate-tree growth relationships of longleaf pine (Pinus plaustris Mill. ) in the southeastern Coastal Plain, USA. Dendrochronologia, 27: 31-43. | |
Huang J , Tardif J , Bergeron Y , et al. Radial growth response of four dominant boreal tree species to climate along a latitudinal gradient in the eastern Canadian boreal forest. Global Change Biology, 2010, 16, 711- 731.
doi: 10.1111/j.1365-2486.2009.01990.x |
|
Kass R E , Raftery A E . Bayes factors. Journal of the American Statistical Association, 1995, 90 (430): 773- 795.
doi: 10.1080/01621459.1995.10476572 |
|
Lhotka J M , Loewenstein E F . An individual-tree diameter growth model for managed uneven-aged oak-shortleaf pine stands in the Ozark Highlands of Missouri, USA. Forest Ecology and Management, 2011, 261 (3): 770- 778.
doi: 10.1016/j.foreco.2010.12.008 |
|
Lu L , Wang H , Chhin S , et al. A Bayesian model averaging approach for modelling tree mortality in relation to site, competition and climatic factors for Chinese fir plantations. Forest Ecology and Management, 2019, 440, 169- 177.
doi: 10.1016/j.foreco.2019.03.003 |
|
Madigan D , Raftery A E . Model selection and accounting for model uncertainty in graphical models using Occam's window. Journal of the American Statistical Association, 1994, 89 (428): 111- 196. | |
Murphy M , Wang D . Do previous birth interval and mother's education influence infant survival? A Bayesian model averaging analysis of Chinese data. Population studies, 2001, 55 (1): 37- 47.
doi: 10.1080/00324720127679 |
|
Picard N , Henry M , Mortier F , et al. Using Bayesian model averaging to predict tree aboveground biomass in tropical moist forests. Forest Science, 2012, 58 (1): 15- 23.
doi: 10.5849/forsci.10-083 |
|
Raftery A E , Madigan D , Hoeting J A . Bayesian model averaging for linear regression models. Journal of the American Statistical Association, 1991, 92 (437): 179- 191. | |
Raftery A E . Approximate Bayes factors and accounting for model uncertainty in generalised linear models. Biometrika, 1996, 83 (2): 251- 266.
doi: 10.1093/biomet/83.2.251 |
|
Raftery A E , Painter I S , Volinsky C T . BMA: an R package for Bayesian model averaging. R News, 2005, 5 (1): 2- 8. | |
Viallefont V , Raftery A E , Richardon S . Variable selection and Bayesian model averaging in case-control studies. Statistics in Medicine, 2001, 20, 3215- 3230.
doi: 10.1002/sim.976 |
|
Volinsky C , Madigan D , Raftery A , et al. Bayesian model averaging in proportional hazard models: assessing the risk of a stroke. Journal of the Royal Statistical Society, 1997, 46, 433- 448.
doi: 10.1111/1467-9884.00095 |
|
Wang M , Liu Q , Fu L , et al. Airborne LiDAR-derived aboveground biomass estimates using a hierarchical Bayesian approach. Remote Sensing, 2019, 11, 1050.
doi: 10.3390/rs11091050 |
|
Wang T , Hamann A , Spittlehouse D L , et al. Climate WNA-high-resolution spatial climate data for western north America. Journal of Applied Meteorology and Climatology, 2012, 51 (1): 16- 29.
doi: 10.1175/JAMC-D-11-043.1 |
|
Zapata-Cuartas M , Sierra C A , Alleman L . Probability distribution of allometric coefficients and Bayesian estimation of aboveground tree biomass. Forest Ecology and Management, 2012, 277, 173- 179.
doi: 10.1016/j.foreco.2012.04.030 |
|
Zhang X , Duan A , Dong L , et al. The application of Bayesian model averaging in compatibility of stand basal area for even-aged plantations in southern China. Forest Science, 2014, 60 (4): 645- 651.
doi: 10.5849/forsci.13-034 |
|
Zhang X , Cao Q V , Duan A , et al. Modeling tree mortality in relation to climate, initial planting density, and competition in Chinese fir plantations using a Bayesian logistic multilevel method. Canada Journal of Forest Research, 2017, 47, 1278- 1285.
doi: 10.1139/cjfr-2017-0215 |
|
Zhang X , Lei Y , Liu X . Modeling stand mortality using Poisson mixture models with mixed-effects. iForest-Biogeosciences and Forestry, 2015, 8, 333- 338.
doi: 10.3832/ifor1022-008 |
[1] | Jinchi Wang,Qinglin Huang,Minghai Yan,Ruchu Huang,Qunrui Zheng. Characteristics of 13-Year-Old Cyclobalanopsis glauca Natural Forest Converted from Eucalyptus grandis Plantation [J]. Scientia Silvae Sinicae, 2021, 57(9): 13-20. |
[2] | Jinchi Wang,Qinglin Huang,Minghai Yan,Ruchu Huang,Qunrui Zheng. Characteristics of 7-Year-Old Castanopsis fargesii Natural Forest Converted from Eucalyptus dunnii Plantation [J]. Scientia Silvae Sinicae, 2021, 57(1): 12-19. |
[3] | Yadong Li,Minglan Cao,Changqing Li,Zhongke Feng. Partition Matching Strategy of UAV Aerial Photographic Images in Forests Based on POS Constraints [J]. Scientia Silvae Sinicae, 2020, 56(10): 113-120. |
[4] | Baoguo Yang, Hongyan Jia, Jian Hao, Yunxing Li, Shengjiang Pang, Shiling Liu, Pei Zhang, Changhai Niu, Daoxiong Cai. Growth Variation of Heartwood and Sapwood of Teak (Tectona grandis) Plantations at Different Ages [J]. Scientia Silvae Sinicae, 2020, 56(1): 65-73. |
[5] | Yingkai Zhang,Pengju Liu,Changchun Liu,Yi Ren. Prediction Method of Cunninghamia lanceolata Growth Based on Spatial Clustering [J]. Scientia Silvae Sinicae, 2019, 55(11): 137-144. |
[6] | Wang Dongzhi, Zhang Dongyan, Li Yongning, Zhang Zhidong, Li Dayong, Huang Xuanrui. Height-Diameter Relationship for Conifer Mixed Forest Based on Bayesian Nonlinear Mixed-Effects Model [J]. Scientia Silvae Sinicae, 2019, 55(11): 85-94. |
[7] | Li Dan, Zhang Junjie, Zhao Mengxi. Extraction of Stand Factors in UAV Image Based on FCM and Watershed Algorithm [J]. Scientia Silvae Sinicae, 2019, 55(5): 180-187. |
[8] | Xie Zhegen, Han Guokang, Tong Hongwei, Xu Jun, Ge Wenning, He Biting. Sequential Forest Price and Its Application in Forest Resources Assets Appraisal [J]. Scientia Silvae Sinicae, 2016, 52(6): 43-53. |
[9] | Wang Dongzhi, Zhang Dongyan, Zhang Zhidong, Huang Xuanrui. Height-Diameter Relationship for Conifer Mixed Forest Based on Nonlinear Mixed-Effects Model [J]. Scientia Silvae Sinicae, 2016, 52(1): 30-36. |
[10] | Qi Yujiao, Li Fengri. Remote Sensing Estimation of Aboveground Forest Carbon Storage in Daxing'an Mountains Based on KNN Method [J]. Scientia Silvae Sinicae, 2015, 51(5): 46-55. |
[11] | Yu Ying, Fan Wenyi, Yang Xiguang. Estimation of Forest NPP in Xiaoxing'an Mountains in 1901—2008 [J]. Scientia Silvae Sinicae, 2014, 50(10): 16-23. |
[12] | Xiao Shengling, Yang Jialong. Individual Tree Aboveground Biomass of Larix gmelinii Natural Forest in the Northern Greater Khingan Mountains [J]. Scientia Silvae Sinicae, 2014, 50(8): 22-29. |
[13] | Zhang Xiongqing, Zhang Jianguo, Duan Aiguo. Compatibility of Stand Volume Model for Chinese Fir Based on Tree-Level and Stand-Level [J]. Scientia Silvae Sinicae, 2014, 50(1): 82-87. |
[14] | Jiang Xian, Zhang Huaiqing, Ju Hongbo, Song Jiehua, Qin Yangping, Wu Shulei. Stand Growth Distribution Model Based on Individual Tree’s Integrated Competition [J]. Scientia Silvae Sinicae, 2013, 49(10): 54-57. |
[15] | Xu Xiaojun, Zhou Guomo, Du Huaqiang, Zhou Yufeng, Hu Junguo, Lu Guofu. Effects of Sample Plots Stratification on Estimation Accuracy of Aboveground Carbon Storage for Phyllostachys edulis Forests [J]. Scientia Silvae Sinicae, 2013, 49(6): 18-24. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||