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首页> 外文期刊>South-East European Forestry >Predicting Diameter at Breast Height from Stump Measurements of Removed Trees to Estimate Cuttings, Illegal Loggings and Natural Disturbances
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Predicting Diameter at Breast Height from Stump Measurements of Removed Trees to Estimate Cuttings, Illegal Loggings and Natural Disturbances

机译：预测乳房高度的直径从去除树桩测量到估计切割，非法测井和自然扰动

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Predicting diameter at breast height (DBH) of trees from stump information may be necessary to reconstruct silvicultural practices, to assess harvested timber and wood, or to estimate forest products’ losses caused by illegal cuttings or natural disasters (disturbances). A model to predict DBH of felled trees was developed by the first Italian National Forest Inventory in 1985 (IFNI85). The model distinguished between the two broad groups of conifers and broadleaves and used stump diameter as the sole quantitative variable. Using an original dataset containing data from about 1200 trees of sixteen species recorded throughout Italy, we assessed the performance of that model. To improve the prediction of the DBH of removed trees over large areas and for multiple species, we developed new models using the same dataset. Performance of the new models was tested through indices computed on cross-validated data obtained through the leave-one-out method. A new model that performs better than the old one was finally selected. Compared to the old NFI model, the selected model improved DBH prediction for fourteen species up to 31.28%. This study proved that species specification and stump height are variables needed to improve the models’ performance and suggested that data collection should be continued to get enhanced models, accurate for different ecological and stand conditions.
机译：从树桩信息中预测乳房高度（DBH）的直径可能是重建造林实践的必要条件，以评估收获的木材和木材，或估计由非法扦插或自然灾害（干扰）引起的林产品的损失。 1985年第一个意大利国家森林清单（IFNI85）开发了一种预测鼠标DBH的模型。该模型区分两组宽组针叶纤维和阔叶物和使用的树桩直径作为唯一的定量变量。使用包含在意大利录制的大约1200棵树的10棵树的原始数据集，我们评估了该模型的性能。为了改善大面积和多种物种的删除树DBH的预测，我们开发了使用相同数据集的新模型。通过在通过休假方法获得的交叉验证数据上计算的指标测试新模型的性能。最终选择了比旧的模型更好。与旧的NFI模型相比，所选模型改善了高达31.28％的十四种的DBH预测。本研究证明，物种规范和树桩高度是提高模型的性能所需的变量，并建议应继续进行数据收集以获得增强型号，准确地进行不同的生态和站立条件。

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