Volume models for Sal (Shorea robusta Gaertn.) in far-western Terai of Nepal

T. Subedi
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引用次数: 8

Abstract

Sal (Shorea robusta Gaertn.) is one of the most important commercial tree species in Nepal and far-western Terai is renowned for its forest. This study was carried out in far-western Terai to develop volume models of Sal at tree level using destructive sampling. Out of 99 sample trees, 81 data were used to develop the models and 18 data for validation of the selected models. Over bark stem diameters were measured at an interval of 0.5 m in lowermost three sections, at an interval of 1 m for one section and at an interval of 2 m in upper part of the trunk from the ground level. Smalian’s formula was used to compute tree volume. Seven regression models were tested using DBH as a predictor variable. Cross validation of the independent data set was used to validate the selected models. The graphical analysis and fit statistics of the models were evaluated to select the best fit model. The selected model for total over bark stem volume is ln V = - 8.04674 + 2.26641 ln DBH with R2 of 92 % and standard error of 0.18. Similarly, the selected models for over bark volume up to 10 and 20 cm top diameter have R2 of 82.41% and 79.97% and standard errors of 0.35 and 0.42, respectively. The prediction error of the selected model was found to be less than 6%. Forest managers can use the recommended model in estimation of timber volume of Sal in a particular forest area of this region for effective forest management. Banko JanakariA Journal of Forestry Information for NepalVol. 27, No. 2, 2017, page: 3-11
尼泊尔Terai西部Sal(Shorea robusta Gaertn)的体积模型
Sal(Shorea robusta Gaertn.)是尼泊尔最重要的商业树种之一,特莱西部以其森林而闻名。这项研究是在遥远的西部Terai进行的,目的是使用破坏性采样在树木水平上开发Sal的体积模型。在99个样本树中,81个数据用于开发模型,18个数据用于验证所选模型。在最下面的三个节段中以0.5m的间隔、一个节段以1m的间隔和树干上部以2m的间隔测量树皮上茎的直径。用斯马利安公式计算树木体积。使用DBH作为预测变量对七个回归模型进行了测试。独立数据集的交叉验证用于验证所选模型。对模型的图形分析和拟合统计进行了评估,以选择最佳拟合模型。总树干体积的选择模型为ln V=-8.04674+2.26641 ln DBH,R2为92%,标准误差为0.18。类似地,顶部直径达10厘米和20厘米的树皮体积的选定模型的R2分别为82.41%和79.97%,标准误差分别为0.35和0.42。所选模型的预测误差小于6%。森林管理者可以使用推荐的模型来估计该地区特定林区的萨尔木材量,以进行有效的森林管理。Banko JanakariA尼泊尔林业信息杂志Vol。2017年2月27日,第3-11页
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