Wang Jin Chi, Deng Hua Feng, Huang Guo Sheng, Wang Xue Jun, Zhang Lu
College of Forestry, Beijing Forestry University, Beijing 100083, China.
Academy of Forest Inventory and Planning, State Forestry Administration, Beijing 100714, China.
Ying Yong Sheng Tai Xue Bao. 2017 Oct;28(10):3189-3196. doi: 10.13287/j.1001-9332.201710.018.
By using nonlinear measurement error method, the compatible tree volume and above ground biomass equations were established based on the volume and biomass data of 150 sampling trees of natural spruce (Picea asperata). Two approaches, controlling directly under total aboveground biomass and controlling jointly from level to level, were used to design the compatible system for the total aboveground biomass and the biomass of four components (stem, bark, branch and foliage), and the total ground biomass could be estimated independently or estimated simultaneously in the system. The results showed that the R of the one variable and bivariate compatible tree volume and aboveground biomass equations were all above 0.85, and the maximum value reached 0.99. The prediction effect of the volume equations could be improved significantly when tree height was included as predictor, while it was not significant in biomass estimation. For the compatible biomass systems, the one variable model based on controlling jointly from level to level was better than the model using controlling directly under total above ground biomass, but the bivariate models of the two methods were similar. Comparing the imitative effects of the one variable and bivariate compatible biomass models, the results showed that the increase of explainable variables could significantly improve the fitness of branch and foliage biomass, but had little effect on other components. Besides, there was almost no difference between the two methods of estimation based on the comparison.
利用非线性测量误差方法,基于150株天然云杉(Picea asperata)采样木的材积和生物量数据,建立了材积与地上生物量兼容方程。采用直接控制总地上生物量和逐级联合控制两种方法,设计了总地上生物量与四个组分(树干、树皮、树枝和树叶)生物量的兼容系统,在该系统中总地上生物量可以独立估计或同时估计。结果表明,一元和二元兼容材积与地上生物量方程的R均在0.85以上,最大值达到0.99。当将树高作为预测变量时,材积方程的预测效果可显著提高,而在生物量估计中则不显著。对于兼容生物量系统,逐级联合控制的一元模型优于直接控制总地上生物量下的模型,但两种方法的二元模型相似。比较一元和二元兼容生物量模型的模拟效果,结果表明,可解释变量的增加能显著提高树枝和树叶生物量的拟合度,但对其他组分影响较小。此外,基于比较的两种估计方法之间几乎没有差异。