Department of Forest Yield and Biometry, Faculty of Forestry, Istanbul University-Cerrahpaşa, 34473, Bahcekoy, Sariyer, Istanbul, Turkey.
Department of Soil Science and Ecology, Faculty of Forestry, Istanbul University-Cerrahpaşa, 34473, Bahcekoy, Sariyer, Istanbul, Turkey.
Environ Monit Assess. 2021 Oct 17;193(11):728. doi: 10.1007/s10661-021-09524-x.
The research was carried out in the coppice-originated pure oak stands that are being converted to high forests in northwest Turkey. The main goal of the research was to determine the bark thickness (BT) based on tree variables, such as tree diameter at breast height (DBH), total tree height (H), crown diameter (CD), and age (AGE) of the stem sections taken from a total of 350 trees that were destructively sampled from different sites, different oak species (Quercus petraea, Quercus frainetto, Quercus cerris), and different development stages. Models were developed with stepwise multiple regression analysis to predict BT based on the variables. For all oak species, all models obtained by stepwise multiple regression analysis were found to be significant at p = 0.001 level. In Quercus petraea, only the DBH-dependent model explained the variation in BT at a rate of 73%, estimating with an absolute error rate of 21%. The fit statistics of the models (based on DBH and DBH-H explanatory variables) obtained for Quercus frainetto are very close to each other, and they explained the variation in BT at a rate of 69% and estimated with an error rate of 26%. Models (based on DBH and DBH-H explanatory variables) explain the variation in BT in Turkey oak at a rate of 91%, indicating species-specific results. The models based on only DBH can be used with high accuracy to estimate BT.
该研究在土耳其西北部正在从萌生林向成熟林转变的萌生栎纯林中进行。研究的主要目的是根据树木变量（如胸径、总树高、冠幅和树干节段年龄）确定树皮厚度（BT），共从 350 棵不同地点、不同栎属树种（栓皮栎、欧洲栓皮栎、克里木栓皮栎）和不同发育阶段的树木中破坏性采集了树干节段样本。采用逐步多元回归分析建立模型，根据变量预测 BT。对于所有的栎属树种，逐步多元回归分析得到的所有模型在 p=0.001 水平上均显著。在栓皮栎中，只有依赖于胸径的模型能够以 73%的比率解释 BT 的变化，估计的绝对误差率为 21%。对于欧洲栓皮栎，基于 DBH 和 DBH-H 解释变量的模型的拟合统计数据非常接近，能够以 69%的比率解释 BT 的变化，并估计误差率为 26%。基于 DBH 和 DBH-H 解释变量的模型能够以 91%的比率解释 BT 的变化，表明了该物种的特异性结果。仅基于 DBH 的模型可以高度准确地估计 BT。
