预测南欧主要森林物种基底面积增加的单株线性混合效应模型

IF 0.8 4区 农林科学 Q3 FORESTRY Forest Systems Pub Date : 2020-12-29 DOI:10.5424/fs/2020293-15500
L. Di Cosmo, D. Giuliani, M. M. Dickson, P. Gasparini
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引用次数: 0

摘要

研究目的。对增长情况的评估对于支持森林管理和森林政策的可持续性至关重要。该研究的目的是开发一个特定物种的模型,通过森林调查记录的变量来预测树木基底面积的年度增量,直接或在更复杂的森林生长和产量模拟模型的背景下评估森林生长。研究领域。意大利。材料和方法。使用了意大利国家森林目录5162个地块中收集的31种不同森林物种的34638棵树的数据;这些数据是在2004年至2006年间记录的。为了说明由于图中嵌套的树导致的数据的层次结构,使用了两级混合效应建模方法。主要结果。最终的结果是一个以物种为虚拟变量的单株线性混合效应模型。树的大小是主要的预测因素,但该模型也集成了地理和地形预测因素,并包括竞争因素。模型拟合良好(McFadden’s Pseudo-R2 0.536),随机效应在小区水平上的方差显著(类内相关系数0.512)。与普通最小二乘回归相比,混合效应模型使小区估计的平均绝对误差平均降低64.5%。研究亮点。利用森林清查数据建立了一个预测不同物种基底面积增量的单株水平模型。用于建模的数据涵盖了31个物种和各种各样的生长条件,该模型似乎适用于更广泛的南欧背景。关键词:树木生长;森林生长模型;森林清查;层次数据结构;意大利。使用的缩写词:BA——基底面积;BAI——5年周期性基底面积增量;BALT——大于主题树的树木的基底面积;BASPratio——受试树种基底面积与林分基底面积之比;BASTratio——受试树木基底面积与林分基底面积之比;CRATIO——冠比;DBH——乳腺高度处的直径;DBH0——调查年前5年对应的乳腺高度处的直径;DBHt——调查年份测量的乳腺高度处的直径;DI5-五年,树皮内,DBH增量;HDOM——优势高度;土地利用、土地利用的变化和林业;ME——平均误差;MAE——平均绝对误差;MPD——平均偏差百分比;MPSE——平均标准误差百分比;NFI——国家森林目录;OLS——普通最小二乘回归;RMSE——均方根误差;UNFCCC-联合国气候变化框架公约。
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An individual-tree linear mixed-effects model for predicting the basal area increment of major forest species in Southern Europe
Aims of the study. Assessment of growth is essential to support sustainability of forest management and forest policies. The objective of the study was to develop a species-specific model to predict the annual increment of tree basal area through variables recorded by forest surveys, to assess forest growth directly or in the context of more complex forest growth and yield simulation models.Area of the study. Italy.Material and methods. Data on 34638 trees of 31 different forest species collected in 5162 plots of the Italian National Forest Inventory were used; the data were recorded between 2004 and 2006. To account for the hierarchical structure of the data due to trees nested within plots, a two-level mixed-effects modelling approach was used.Main results. The final result is an individual-tree linear mixed-effects model with species as dummy variables. Tree size is the main predictor, but the model also integrates geographical and topographic predictors and includes competition. The model fitting is good (McFadden’s Pseudo-R2 0.536), and the variance of the random effect at the plot level is significant (intra-class correlation coefficient 0.512). Compared to the ordinary least squares regression, the mixed-effects model allowed reducing the mean absolute error of estimates in the plots by 64.5% in average.Research highlights. A single tree-level model for predicting the basal area increment of different species was developed using forest inventory data. The data used for the modelling cover 31 species and a great variety of growing conditions, and the model seems suitable to be applied in the wider context of Southern Europe.   Keywords: Tree growth; forest growth modelling; forest inventory; hierarchical data structure; Italy.Abbreviations used: BA - basal area; BAI – five-year periodic basal area increment; BALT - basal area of trees larger than the subject tree; BASPratio - ratio of subject tree species basal area to stand basal area; BASTratio - ratio of subject tree basal area to stand basal area; CRATIO - crown ratio; DBH – diameter at breast height ; DBH0– diameter at breast height corresponding to five years before the survey year; DBHt– diameter at breast height measured in the survey year; DI5 - five-year, inside bark, DBH increment; HDOM - dominant height; LULUCF - Land Use, Land Use Changes and Forestry; ME - mean error; MAE - mean absolute error; MPD - mean percent deviation; MPSE - mean percent standard error; NFI(s) - National Forest Inventory/ies; OLS - ordinary least squares regression; RMSE - root mean squared error; UNFCCC - United Nation Framework Convention on Climate Change.
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来源期刊
Forest Systems
Forest Systems FORESTRY-
CiteScore
1.40
自引率
14.30%
发文量
30
审稿时长
6-12 weeks
期刊介绍: Forest Systems is an international peer-reviewed journal. The main aim of Forest Systems is to integrate multidisciplinary research with forest management in complex systems with different social and ecological background
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