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Development of Crown Profile Models for Chinese Fir Using Non-linear Mixed-Effects Modelling

机译:非线性混合效应模型的杉木冠形模型开发

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Crown profile models are key components of growth and yield models and are crucial for estimating the crown volume and constructing 3D visualization of trees. We used a total of 431 trees collected from 98 pure even-aged temporary sample plots established in Fujian Province to develop crown profile models of Chinese fir (Cunninghamia lanceolata).To describe the shape of tree crowns more accurately, significance tests of the effects of different stand conditions (stand age, site index, and stand density) on crown shape were conducted with one-way analysis of variance (ANOVA). Multiple comparisons based on the ANOVA results were used to classify the crown data into three groups according to stand age: Group I (young forest), Group II (medium forest), and Group III (nearly mature and mature forest). We analysed the relationships between the crown variables and stand variables and used the reparameterization approach to develop three optimal crown profile models for different age groups. Stand variables (such as stand density) further improved the prediction efficacy of the models. Considering the correlation between repeated measurement data for the same tree crown, the non-linear mixed-effects modelling (NLME) method was used to account for autocorrelation. The determination coefficients (R 2 ) of the above three optimal models fitted by the non-linear mixed-effects approach were 0.9214, 0.9398 and 0.9129, and their Root Mean Squared Errors (RMSEs) were 0.1246, 0.1409 and 0.1786, respectively. The determinant coefficients (R 2 ) of the three models fitted by the non-linear least squares (NLS) approach were 0.9015, 0.8794 and 0.8930, and their RMSEs were 0.1395, 0.2102 and 0.1878, respectively. The results indicated that the predicted accuracy was significantly increased by using non-linear mixed effects modelling compared with the NLS method.
机译:树冠轮廓模型是生长和产量模型的关键组成部分,对于估算树冠体积和构建树木的3D可视化至关重要。我们利用从福建省建立的98个纯均匀年龄临时样地收集的431棵树来开发杉木树冠轮廓模型。为了更准确地描述树冠的形状,对树冠的效果进行了显着性检验通过单向方差分析(ANOVA)对冠状的不同林分条件(林分年龄,站点指数和林分密度)进行了研究。使用基于方差分析结果的多次比较,根据林分年龄将树冠数据分为三类:第一类(幼林),第二类(中林)和第三类(几乎成熟的林)。我们分析了树冠变量和林分变量之间的关系,并使用重新参数化方法为不同年龄组开发了三种最佳树冠轮廓模型。林分变量(例如林分密度)进一步提高了模型的预测效力。考虑到同一棵树冠的重复测量数据之间的相关性,非线性混合效果建模(NLME)方法用于说明自相关。通过非线性混合效应方法拟合的上述三个最佳模型的确定系数(R 2)为0.9214、0.9398和0.9129,其均方根误差(RMSE)分别为0.1246、0.1409和0.1786。非线性最小二乘法(NLS)拟合的三个模型的行列式系数(R 2)为0.9015、0.8794和0.8930,其RMSE分别为0.1395、0.2102和0.1878。结果表明,与NLS方法相比,使用非线性混合效应建模可以显着提高预测精度。

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