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首页> 外文期刊>iForest: Biogeosciences and Forestry >Comparison of parametric and nonparametric methods for modeling height-diameter relationships
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Comparison of parametric and nonparametric methods for modeling height-diameter relationships

机译：参数化方法与非参数化方法对高度-直径关系建模的比较

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Abstract: This paper focuses on the problem of regionalization of the height-diameter model at the stand level. To this purpose, we selected two different modeling techniques. As a parametric method, we chose a linear mixed effects model (LME) with calibrated conditional prediction, whose calibration was carried out on randomly selected trees either close to mean diameter or within three diameter intervals throughout the diameter range. As a nonparametric method, the technique of classification and regression trees (CART) was chosen. These two methods were also compared with the local model created by ordinary least squares regression. The results show that LME with calibrated conditional prediction based on measurements of height at three diameter intervals provided results very close to the local model, especially when six to nine trees are measured. We recommend this technique for the regionalization of the global model. The CART method provided worse results than LME, with the exception of parameters of the residual distribution. Nevertheless, the latter approach is very user-friendly, as the regression tree creation and especially its interpretation are relatively simple, and could be recommended when larger deviations are allowed.
机译：摘要：本文重点探讨了展位高度直径模型的区域化问题。为此，我们选择了两种不同的建模技术。作为一种参数方法，我们选择了带有校正条件预测的线性混合效应模型（LME），其校正是在随机选择的树木上进行的，这些树木接近平均直径或在整个直径范围内的三个直径间隔内。作为非参数方法，选择了分类和回归树（CART）技术。还将这两种方法与通过普通最小二乘回归创建的局部模型进行了比较。结果表明，基于校准的条件预测的LME基于三个直径间隔处的高度测量值，提供的结果与本地模型非常接近，尤其是当测量六到九棵树时。我们建议将此技术用于全局模型的区域化。除了残差分布的参数外，CART方法提供的结果比LME差。但是，后一种方法非常用户友好，因为回归树的创建（尤其是其解释）相对简单，可以在允许较大偏差的情况下建议使用。

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