Li Yan-qiong, Deng Xiang-wen, Huang Zhi-hong, Xiang Wen-hua, Yan Wen-de, Lei Pi-feng, Zhou Xiao-lu, Peng Chang-hui
Faculty of Life Science and Technology, Central South University of Forestry and Technology, Changsha, Hunan, 410004, China.
Faculty of Life Science and Technology, Central South University of Forestry and Technology, Changsha, Hunan, 410004, China; National Engineering Laboratory for Applied Technology of Forestry & Ecology in South China, Changsha, Hunan, 410004, China.
PLoS One. 2015 Apr 23;10(4):e0125118. doi: 10.1371/journal.pone.0125118. eCollection 2015.
Tree diameter at breast height (dbh) and height are the most important variables used in forest inventory and management as well as forest carbon-stock estimation. In order to identify the key stand variables that influence the tree height-dbh relationship and to develop and validate a suit of models for predicting tree height, data from 5961 tree samples aged from 6 years to 53 years and collected from 80 Chinese-fir plantation plots were used to fit 39 models, including 33 nonlinear models and 6 linear models, were developed and evaluated into two groups. The results showed that composite models performed better in height estimate than one-independent-variable models. Nonlinear composite Model 34 and linear composite Model 6 were recommended for predicting tree height in Chinese fir plantations with a dbh range between 4 cm and 40 cm when the dbh data for each tree and the quadratic mean dbh of the stand (Dq) and mean height of the stand (Hm) were available. Moreover, Hm could be estimated by using the formula Hm = 11.707 × l n(Dq)-18.032. Clearly, Dq was the primary stand variable that influenced the height-dbh relationship. The parameters of the models varied according to stand age and site. The inappropriate application of provincial or regional height-dbh models for predicting small tree height at local scale may result in larger uncertainties. The method and the recommended models developed in this study were statistically reliable for applications in growth and yield estimation for even-aged Chinese-fir plantation in Huitong and Changsha. The models could be extended to other regions and to other tree species only after verification in subtropical China.
胸径(dbh)和树高是森林资源清查与管理以及森林碳储量估算中最重要的变量。为了确定影响树高-胸径关系的关键林分变量,并开发和验证一套预测树高的模型,利用从80个杉木人工林样地采集的5961个年龄在6年至53年之间的树木样本数据,拟合了39个模型,包括33个非线性模型和6个线性模型,并将其分为两组进行开发和评估。结果表明,复合模型在树高估计方面比单变量模型表现更好。当可获取每棵树的胸径数据、林分的二次平均胸径(Dq)和林分平均树高(Hm)时,推荐使用非线性复合模型34和线性复合模型6来预测胸径在4厘米至40厘米之间的杉木人工林树高。此外,Hm可以通过公式Hm = 11.707 × ln(Dq) - 18.032来估算。显然,Dq是影响树高-胸径关系的主要林分变量。模型参数随林分年龄和立地条件而变化。在地方尺度上不适当地应用省级或区域树高-胸径模型来预测小树高度可能会导致更大的不确定性。本研究中开发的方法和推荐模型在统计上对于会同和长沙同龄杉木人工林的生长和产量估计应用是可靠的。这些模型只有在亚热带中国经过验证后,才能扩展到其他地区和其他树种。