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Height-Diameter Modeling of Cinnamomum tamala Grown in Natural Forest in Mid-Hill of Nepal

机译:尼泊尔中部山地天然林中种植的肉桂肉桂的高径模型

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Tree height (H) and diameter at breast height (D) are key variables to calculate tree volume and biomass. We developed a height-diameter (H-D) model for Cinnamomum tamala by evaluating 18 nonlinear models. Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Root Mean Square Error (RMSE), mean bias, Mean Absolute Error (MAE), graphical appearance, and biological logic were the criteria used to evaluate the predictive performance of the models. Gompertz model (M14) performed the best for predicting the total height of C. tamala trees with the least RMSE (1.742 m), mean bias (0.012 m), and MAE (1.342 m) and satisfied model assumptions and biological logic. Validation data ranked the Gompertz model as the best model with RMSE (1.546 m), mean bias (-0.106 m), and MAE (1.149 m). Despite the consistent performance of the Gompertz model, it tended to underestimate the height prediction for taller (dominant crown class) and larger trees. Further work on refitting and validation of the proposed model with data from a larger geographic area, wider-ranging sites, and stand conditions is recommended.
机译:树高(H)和胸高(D)的直径是计算树体积和生物量的关键变量。通过评估18种非线性模型,我们开发了Cinnamomum tamala的高径(H-D)模型。赤池信息准则(AIC),贝叶斯信息准则(BIC),均方根误差(RMSE),均值偏差,平均绝对误差(MAE),图形外观和生物学逻辑是用于评估模型的预测性能的标准。 Gompertz模型(M14)在预测RM.SESE(1.742µm),平均偏差(0.012µm)和MAE(1.342µm)最少的情况下,能很好地预测印度锥果树的总高度,并满足模型假设和生物学逻辑。验证数据将Gompertz模型评为最佳模型,其均方根误差(1.546µm),平均偏差(-0.106µm)和MAE(1.149µm)。尽管Gompertz模型具有一致的性能,但它往往低估了较高(主要树冠等级)和较大树木的高度预测。建议进行进一步的工作,以使用更大的地理区域,更广泛的站点以及展位条件的数据来对拟议模型进行重新拟合和验证。

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