Predicting Diameter at Breast Height from Stump Measurements of Removed Trees to Estimate Cuttings, Illegal Loggings and Natural Disturbances

IF 0.7 Q3 FORESTRY SEEFOR-South-East European Forestry Pub Date : 2020-06-01 DOI:10.15177/seefor.20-08
L. Cosmo, P. Gasparini
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引用次数: 1

Abstract

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.
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从移除树木的树桩测量预测乳高直径,以估计砍伐、非法伐木和自然干扰
根据树桩信息预测树木的胸径(DBH)对于重建造林实践、评估采伐的木材和木材或估计非法砍伐或自然灾害(干扰)造成的森林产品损失可能是必要的。1985年,第一次意大利国家森林调查(IFNI85)开发了一个预测砍伐树木DBH的模型。该模型区分了针叶树和阔叶树这两大类,并将树桩直径作为唯一的定量变量。使用一个原始数据集,其中包含意大利各地记录的16个物种的约1200棵树的数据,我们评估了该模型的性能。为了改进对大面积和多个物种被移除树木的DBH的预测,我们使用相同的数据集开发了新的模型。通过对通过留一法获得的交叉验证数据计算的指数来测试新模型的性能。最终选择了一种性能比旧型号更好的新型号。与旧的NFI模型相比,所选模型将14个物种的DBH预测提高了31.28%。本研究证明,物种规格和树桩高度是提高模型性能所需的变量,并建议继续收集数据,以获得针对不同生态和林分条件的增强模型。
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来源期刊
CiteScore
1.20
自引率
16.70%
发文量
6
审稿时长
8 weeks
期刊介绍: The primary aim of the SEEFOR journal is to publish original, novel and quality articles and thus contribute to the development of scientific, research, operational and other activities in the field of forestry. Besides scientific, the objectives of the SEEFOR are educational and informative as well. SEEFOR should stimulate intensive professional and academic work, teaching, as well as physical cooperation of institutions and interdisciplinary collaboration, a faster ascendance and affirmation of young scientific personnel. SEEFOR should contribute to the stronger cooperation between the science, practice and society, and to the overall dissemination of the forestry way-of thinking. The scope of the journal’s interests encompasses all ecological, economical, technical, technological, social and other aspects of forestry and wood technology. The journal is open for publishing research from all geographical zones and study locations, whether they are conducted in natural forests, plantations or urban environments, as long as methods used in the research and obtained results are of high interest and importance to South-east European and international forestry.
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