Scientia Silvae Sinicae ›› 2022, Vol. 58 ›› Issue (5): 31-39.doi: 10.11707/j.1001-7488.20220504
Previous Articles Next Articles
Zipeng Zhang,Junjie Wang,Suoming Liu,Lichun Jiang*
Received:
2021-04-16
Online:
2022-05-25
Published:
2022-08-19
Contact:
Lichun Jiang
CLC Number:
Zipeng Zhang,Junjie Wang,Suoming Liu,Lichun Jiang. Effect of Form Quotient on Prediction Accuracy of Individual Tree Volume and Biomass of Betula platyphylla[J]. Scientia Silvae Sinicae, 2022, 58(5): 31-39.
Table 2
Parameter estimates and fitting statistics of models"
形率 Form quotient | a1 | a2 | a3 | a4 | RMSE | R2 | ||||||||||
(1) | (2) | (1) | (2) | (1) | (2) | (2) | (1) | (2) | (1) | (2) | ||||||
— | 0.000 7 | 0.000 08 | 1.974 3 | 1.747 3 | 1.000 2 | 0.054 1 | 0.046 7 | 0.943 4 | 0.957 8 | |||||||
q0 | 0.000 7 | 0.000 08 | 1.967 2 | 1.731 8 | -0.090 1 | 1.011 4 | -0.154 6 | 0.054 0 | 0.046 4 | 0.943 5 | 0.958 3 | |||||
q0.02 | 0.000 7 | 0.000 07 | 1.970 0 | 1.747 6 | -0.127 2 | 1.003 7 | 0.029 3 | 0.054 0 | 0.046 6 | 0.943 5 | 0.957 9 | |||||
q0.04 | 0.000 7 | 0.000 07 | 1.967 8 | 1.746 5 | -0.343 0 | 1.013 4 | 0.094 8 | 0.053 8 | 0.046 6 | 0.943 9 | 0.957 9 | |||||
q0.06 | 0.000 8 | 0.000 07 | 1.948 5 | 1.747 8 | -0.555 7 | 1.003 2 | 0.026 7 | 0.053 7 | 0.046 7 | 0.944 2 | 0.957 8 | |||||
q0.08 | 0.000 5 | 0.000 06 | 2.055 0 | 1.823 1 | 1.056 2 | 1.029 7 | 1.153 0 | 0.051 9 | 0.043 6 | 0.947 8 | 0.963 2 | |||||
q0.1 | 0.000 4 | 0.000 03 | 2.192 1 | 1.966 6 | 1.790 3 | 1.065 2 | 1.934 1 | 0.045 4 | 0.034 8 | 0.960 1 | 0.976 5 | |||||
q0.15 | 0.000 3 | 0.000 03 | 2.225 3 | 2.006 3 | 1.597 2 | 1.048 3 | 1.710 1 | 0.042 0 | 0.030 5 | 0.965 8 | 0.981 9 | |||||
q0.2 | 0.000 4 | 0.000 06 | 2.205 0 | 1.993 3 | 1.706 9 | 0.874 0 | 1.624 6 | 0.038 7 | 0.030 5 | 0.970 9 | 0.981 9 | |||||
q0.3 | 0.000 5 | 0.000 08 | 2.160 1 | 1.945 1 | 1.554 9 | 0.891 2 | 1.500 5 | 0.038 1 | 0.029 3 | 0.971 9 | 0.983 3 | |||||
q0.4 | 0.000 6 | 0.000 10 | 2.160 0 | 1.959 5 | 1.431 8 | 0.814 2 | 1.357 9 | 0.033 3 | 0.024 8 | 0.978 4 | 0.988 0 | |||||
q0.5 | 0.000 5 | 0.000 06 | 2.165 5 | 1.947 8 | 0.896 5 | 0.983 8 | 0.891 7 | 0.037 4 | 0.026 0 | 0.972 8 | 0.986 9 | |||||
q0.6 | 0.000 7 | 0.000 07 | 2.081 0 | 1.865 1 | 0.559 0 | 0.992 0 | 0.561 8 | 0.040 8 | 0.030 0 | 0.967 8 | 0.982 5 | |||||
q0.7 | 0.000 7 | 0.000 08 | 2.105 4 | 1.893 9 | 0.447 4 | 0.952 1 | 0.438 2 | 0.038 6 | 0.028 3 | 0.971 1 | 0.984 4 | |||||
q0.8 | 0.000 7 | 0.000 07 | 2.092 9 | 1.871 3 | 0.320 3 | 1.009 5 | 0.324 0 | 0.041 2 | 0.030 3 | 0.967 1 | 0.982 2 | |||||
q0.9 | 0.000 7 | 0.000 07 | 2.069 2 | 1.837 6 | 0.178 3 | 1.049 2 | 0.190 5 | 0.046 6 | 0.036 7 | 0.957 9 | 0.973 8 |
Table 3
Comparisons of error variance functions of volume models"
误差函数 Error functions | 变量 Variable | AIC | BIC | |||||||
(1) | (2) | (14) | (15) | (1) | (2) | (14) | (15) | |||
指数函数 Exponential function | -1 660.4 | -1 848.0 | -1 823.3 | -2 263.4 | -1 644.6 | -1 828.2 | -1 803.5 | -2 239.7 | ||
V | -1 624.9 | -1 864.8 | -1 802.2 | -2 267.4 | -1 609.1 | -1 845.0 | -1 782.4 | -2 243.7 | ||
-1 705.1 | -1 303.4 | -870.7 | -1 833.2 | -1 689.3 | -1 283.6 | -850.9 | -1 809.5 | |||
D | -1 719.5 | -1 938.9 | -1 888.9 | -2 356.6 | -1 703.7 | -1 919.1 | -1 869.1 | -2 332.8 | ||
D2H | 5 313.3 | 5 905.8 | 5 333.2 | 5 929.6 | ||||||
幂函数 Power function | -1 737.8 | -1 933.6 | -1 945.4 | -2 393.3 | -1 720.9 | -1 913.8 | -1 925.5 | -2 369.6 | ||
V | -1 700.1 | -1 924.5 | -1 934.3 | -2 411.1 | -1 684.2 | -1 904.7 | -1 914.5 | -2 387.3 | ||
-774.7 | -1 273.3 | -931.3 | -1 892.7 | -758.9 | -1 253.5 | -911.5 | -1 869.0 | |||
D | -774.7 | -1 273.3 | -931.3 | -1 892.7 | -758.9 | -1 253.5 | -911.5 | -1 869.0 | ||
D2H | 5 313.3 | 5 905.8 | 5 333.2 | 5 929.6 | ||||||
常数加幂函数 Constant plus power function | -1 737.7 | -1 937.1 | -1 943.4 | -2 400.5 | -1 717.8 | -1 913.3 | -1 919.6 | -2 372.7 | ||
V | -1 716.7 | -1 945.3 | -1 933.2 | -2 415.3 | -1 696.9 | -1 921.5 | -1 909.5 | -2387.6 | ||
-1 271.3 | -762.6 | -929.2 | -1 890.7 | -752.9 | -1 247.5 | -905.5 | -1 863.0 | |||
D | -1 271.3 | -762.6 | -929.2 | -1 890.7 | -752.9 | -1 247.5 | -905.5 | -1 863.0 | ||
D2H | 5 313.3 | 5 905.8 | 5 333.2 | 5 929.6 |
Table 4
Validation results of volume models"
模型Model | 自变量Independent variable | MAB | MPB | RMSE | R2 |
(1) | D | 0.037 7 | 13.102 8 | 0.055 5 | 0.940 4 |
(2) | D, H | 0.029 6 | 10.372 2 | 0.048 0 | 0.955 4 |
(14) | D, q0.4 | 0.023 3 | 8.220 0 | 0.033 9 | 0.977 7 |
(15) | D, H, q0.4 | 0.015 9 | 5.633 9 | 0.026 0 | 0.986 8 |
(5) | D, H | 0.039 0 | 15.730 5 | 0.063 7 | 0.921 6 |
Table 5
Prediction error of biomass using different volume models"
模型Model | 形率Form quotient | MAB | MPB | RMSE | R2 |
(1) | 无None | 0.020 8 | 12.976 3 | 0.030 3 | 0.944 4 |
(2) | 无None | 0.016 1 | 10.137 8 | 0.026 2 | 0.958 5 |
(14) | q0.4 | 0.012 8 | 8.113 4 | 0.018 6 | 0.978 9 |
(15) | q0.4 | 0.008 6 | 5.438 8 | 0.013 9 | 0.988 2 |
(5) | 无None | 0.021 8 | 15.743 8 | 0.035 7 | 0.922 9 |
陈振雄, 贺东北, 肖前辉, 等. 西藏冷杉立木生物量和材积模型研建. 中南林业科技大学学报, 2018, 38 (1): 16- 21. | |
Chen Z X , He D B , Xiao Q H , et al. Establishment of biomass and tree volume equations for Abies in Tibet. Journal of Central South University of Forestry & Technology, 2018, 38 (1): 16- 21. | |
靳晓东, 姜立春. 基于树干不同形率的樟子松立木材积方程研建. 北京林业大学学报, 2020, 42 (3): 78- 86. | |
Jin X D , Jiang L C . Equation construction on standing tree volume of Pinus sylvestris var. mongolica based on different form quotients of trunk. Journal of Beijing Forestry University, 2020, 42 (3): 78- 86. | |
李海奎. 中国森林植被生物量和碳储量评估. 北京: 中国林业出版社, 2010. | |
Li H K . Estimation and evaluation of forest biomass carbon storage in China. Beijing: China Forestry Publishing House, 2010. | |
李海奎, 赵鹏祥, 雷渊才, 等. 基于森林清查资料的乔木林生物量估算方法的比较. 林业科学, 2012, 48 (5): 44- 52. | |
Li H K , Zhao P X , Lei Y C , et al. Comparison on estimation of wood biomass using forest inventory data. Scientia Silvae Sinicae, 2012, 48 (5): 44- 52. | |
李晖, 曾伟生. 不同区域落叶松二元立木材积表的检验及差异分析. 林业科学, 2016, 52 (6): 157- 162. | |
Li H , Zeng W S . Validation and comparison of two-variable tree volume tables for Larix spp. in different regions of China. Scientia Silvae Sinicae, 2016, 52 (6): 157- 162. | |
Shahzad MuhammadKhurra, 韩斐斐, 姜立春. 不同抽样方法对兴安落叶松立木材积方程预测精度的影响. 林业科学, 2018, 54 (8): 99- 105. | |
Khurra S , Han F F , Jiang L C . Effects of different sampling methods on predict precision of individual tree volume equation for dahurian larch. Scientia Silvae Sinicae, 2018, 54 (8): 99- 105. | |
张明铁. 单株立木材积测定方法的研究. 林业资源管理, 2004, (1): 24- 26.
doi: 10.3969/j.issn.1002-6622.2004.01.006 |
|
Zhang M T . Study on volume measurement of single trees. Forest Resources Management, 2004, (1): 24- 26.
doi: 10.3969/j.issn.1002-6622.2004.01.006 |
|
张明铁, 李万宝. 用干形指数测定单株立木材积的研究. 内蒙古林学院学报, 1995, (1): 43- 45. | |
Zhang M T , Li W B . The reseach of measuring singles volume using tree trunk shape exponent. Journal of Inner Mongolia Forestry College, 1995, (1): 43- 45. | |
曾伟生. 国家森林资源连续清查中的材积估计问题探讨. 中南林业调查规划, 2007, 26 (2): 1- 3.1-3, 6.
doi: 10.3969/j.issn.1003-6075.2007.02.001 |
|
Zeng W S . Discussion on volume estimation in continuous forest inventory in China. Central South Forest Inventory and Planning, 2007, 26 (2): 1- 3.1-3, 6.
doi: 10.3969/j.issn.1003-6075.2007.02.001 |
|
曾伟生. 二元立木材积方程的检验与更新方法探讨. 中南林业调查规划, 2010, 29 (3): 1- 5.
doi: 10.3969/j.issn.1003-6075.2010.03.001 |
|
Zeng W S . Discussion on verification and updating of two-way tree volume equations. Central South Forest Inventory and Planning, 2010, 29 (3): 1- 5.
doi: 10.3969/j.issn.1003-6075.2010.03.001 |
|
曾伟生, 贺东北, 蒲莹, 等. 含地域和起源因子的马尾松立木生物量与材积方程系统. 林业科学, 2019, 55 (2): 75- 86. | |
Zeng W S , He D B , Pu Y , et al. Individual tree biomass and volume equation system with region and origin in variables for Pinus massoniana in China. Scientia Silvae Sinicae, 2019, 55 (2): 75- 86. | |
曾伟生, 夏忠胜, 朱松, 等. 贵州人工杉木相容性立木材积和地上生物量方程的建立. 北京林业大学学报, 2011, 33 (4): 1- 6.
doi: 10.3969/j.issn.1671-6116.2011.04.001 |
|
Zeng W S , Xia Z S , Zhu S , et al. Compatible tree volume and above-ground biomass equations for Chinese fir plantations in Guizhou. Journal of Beijing Forestry University, 2011, 33 (4): 1- 6.
doi: 10.3969/j.issn.1671-6116.2011.04.001 |
|
中华人民共和国农林部. 立木材积表(LY 208—77). 北京: 技术标准出版社, 1978. | |
Agriculture and Forestry Ministry of China . Tree volume tables (LY 208—77). Beijing: Technical Standards Press, 1978. | |
Adesoye P O , Popoola O D . Determinants of stem form: application to Tectona grandis (Linn. F) stands. Journal of Sustainable Forestry, 2016, 35 (5): 338- 354.
doi: 10.1080/10549811.2016.1177730 |
|
Akindele S O , LeMay V M . Development of tree volume equations for common timber species in the tropical rain forest area of Nigeria. Forest Ecology and Management, 2006, 226 (1/2/3): 41- 48. | |
Bi H Q . Improving stem volume estimation of regrowth Eucalyptus fastigata with a lower stem form quotient. Australian Forestry, 1994, 57 (3): 98- 104.
doi: 10.1080/00049158.1994.10676123 |
|
Cao Q V , Wang J . Evaluation of methods for calibrating a tree taper equation. Forest Science, 2014, 61 (2): 213- 219. | |
Chapagain T R , Sharma R P . Modeling form factors for sal (Shorea robusta Gaertn.) using tree and stand measures, and random effects. Forest Ecology and Management, 2021, 482, 118807.
doi: 10.1016/j.foreco.2020.118807 |
|
Dong L H , Widagdo F R A , Xie L F , et al. Biomass and volume modeling along with carbon concentration variations of short-rotation poplar plantations. Forests, 2020, 11 (7): 780.
doi: 10.3390/f11070780 |
|
Dong L H , Liu Y S , Zhang L J , et al. Variation in carbon concentration and allometric equations for estimating tree carbon contents of 10 broadleaf species in natural forests in northeast China. Forests, 2019, 10 (10): 928.
doi: 10.3390/f10100928 |
|
Fraga M P , Barreto P A B , de Paula A . Estimaɕãode volume de Pterogyne nitens em plantio puro no sudoeste da Bahia. Pesquisa Florestal Brasileira, 2014, 34 (79): 207.
doi: 10.4336/2014.pfb.34.79.703 |
|
Kershaw J A Jr , Ducey M J , Beers T W , et al. Forest Mensuration. 5th edn Chichester, UK: John Wiley & Sons, Ltd., 2016. | |
Kong L , Yang H , Kang X , et al. Stand volume equation developed from an experimental form factor with the breast height form quotient. Taiwan Journal of Forest Science, 2012, 27 (4): 357- 367. | |
Lehtonen A , Mäkipää R , Heikkinen J , et al. Biomass expansion factors (BEFs) for Scots pine, Norway spruce and birch according to stand age for boreal forests. Forest Ecology and Management, 2004, 188 (1/2/3): 211- 224. | |
Luoma V , Saarinen N , Kankare V , et al. Examining changes in stem taper and volume growth with two-date 3D point clouds. Forests, 2019, 10 (5): 382. | |
Mbangilwa M M , He P , Jiang L C . Evaluation of ecoregion-based volume equations for Scots pine (Pinus sylvestrix) in the eastern Daxing'an Mountains, northeast China. Applied Ecology and Environmental Research, 2020, 18 (4): 4941- 4958. | |
McRoberts R E , Westfall J A . Effects of uncertainty in model predictions of individual tree volume on large area volume estimates. Forest Science, 2013, 60 (1): 34- 42. | |
Muhairwe C K . Tree form and taper variation over time for interior lodgepole pine. Canadian Journal of Forest Research, 1994, 24 (9): 1904- 1913. | |
Özçelik R , Altinkaya H . Comparison of tree volume equations for brutian pine stands in Eǧirdir district. Türkiye Ormancılık Dergisi, 2019, 20 (3): 149- 156. | |
Özçelik R , Cao Q V , Trincado G , et al. Predicting tree height from tree diameter and dominant height using mixed-effects and quantile regression models for two species in Turkey. Forest Ecology and Management, 2018, 419/420, 240- 248. | |
Panagiotidis D , Abdollahnejad A , Siavík M . Assessment of stem volume on plots using terrestrial laser scanner: a precision forestry application. Sensors, 2021, 21 (1): 301. | |
Pereira J E S , Barreto-Garcia P A B , de Paula A , et al. Form quotient in estimating caatinga tree volume. Journal of Sustainable Forestry, 2021, 40 (5): 508- 517. | |
Schröder T , Costa E A , Valério A F , et al. Taper equations for Pinus elliottii Engelm. in southern Paraná, Brazil. Forest Science, 2015, 61 (2): 311- 319. | |
Turnblom E C , Burk T E . Adjusting volume table estimates using normal form quotient. Canadian Journal of Forest Research, 1996, 26 (1): 155- 158. | |
van den Berge S , Vangansbeke P , Calders K , et al. Biomass expansion factors for hedgerow-grown trees derived from terrestrial LiDAR. BioEnergy Research, 2021, 14 (2): 561- 574. | |
Wang M L , Kane M B , Borders B E , et al. Direct variance-covariance modeling as an alternative to the traditional guide curve approach for prediction of dominant heights. Forest Science, 2013, 60 (4): 652- 662. | |
Zavarzin V V , Lebedev A V , Gemonov A V . Estimation and validation of stem volume equations for Pinus sibirica in Russia. IOP Conference Series: Earth and Environmental Science, 2021, 677 (5): 052117. | |
Zeng W S , Duo H R , Lei X D , et al. Individual tree biomass equations and growth models sensitive to climate variables for Larix spp. in China. European Journal of Forest Research, 2017, 136 (2): 233- 249. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||