Modeling Stand Density Effects on Taper for Jack Pine and Black Spruce Plantations Using Dimensional Analysis
Modeling Stand Density Effects on Taper for Jack Pine and Black Spruce Plantations Using Dimensional Analysis
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DOI：
10.1093/forestscience/55.3.268
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发表时间：
2009-06
期刊：
影响因子：
1.4
通讯作者：
Mahadev Sharma;J. Parton
中科院分区：
文献类型：
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作者：
Mahadev Sharma;J. Parton
利用量纲分析方法，建立了不同密度生长的杰克松和黑云杉的锥度方程。本研究中使用的数据来自于1135棵短叶松（Pinus banksiana Lamb.）和1189棵黑云杉（Picea mariana [Mill.]）的茎分析。在加拿大安大略省北部的北方森林地区，从25个年龄均匀的单一树种种植园中取样的树木。大约一半的树被随机选择用于模型开发，其余的用于模型评估。采用非线性混合效应方法拟合锥度方程。通过加入随机效应参数，提高了模型的预测精度，该参数是基于上茎直径测量的新树。研究了三种使用上管柱直径测量来预测随机效应的预测精度：一种是沿井眼任意高度的直径；两个直径，从胸高的下面和上面各一个；三个直径，一个在下面，另外两个在胸高以上。由于实际原因，测量直径的上高度限制在总树高的65%。对于第一种情况，使用总高度的34 - 38%的直径测量校准的模型提供了树皮内部直径的最佳预测。对于第二种情况，使用树桩附近的一个直径和接近总高度65%的另一个直径校准的模型在预测树皮内部直径方面产生了最小的偏差。对于第三种情况，使用树桩附近的直径和大约占总高度的35%和65%进行校准的模型提供了最高的预测精度。
A taper equation was developed for jack pine and black spruce trees growing at varying density using a dimensional analysis approach. Data used in this study came from stem analysis on 1,135 jack pine (Pinus banksiana Lamb.) and 1,189 black spruce (Picea mariana [Mill.] B.S.P.) trees sampled from 25 even-aged monospecific plantations in the Canadian boreal forest region of Northern Ontario. About half of the trees were randomly selected for model development, with the remainder used for model evaluation. A nonlinear mixed-effects approach was applied in fitting the taper equation. The predictive accuracy of the model was improved by including random-effects parameters for a new tree based on upper stem diameter measurements. Three scenarios of using upper stem diameter measurements to predict random effects were examined for predictive accuracy: one diameter at any height along the bole; two diameters, one each from below and above breast height; and three diameters, one from below and the other two from above breast height. The upper height at which the diameter was measured was limited to 65% of total tree height for practical reasons. For the first scenario, the model calibrated using a diameter measurement from between 34 and 38% of total height provided the best predictions of inside-bark diameters. For the second scenario, the model calibrated using one diameter from near the stump and the other from close to 65% of total height produced the least bias in predicting inside-bark diameters. For the third scenario, the model calibrated using the diameters from near the stump and at approximately 35 and 65% of total height provided the highest prediction accuracy.