Incorporated neighborhood and environmental effects to model individual-tree height using random forest regression

IF 1.8 3区 农林科学 Q2 FORESTRY Scandinavian Journal of Forest Research Pub Date : 2023-05-19 DOI:10.1080/02827581.2023.2215545
Jiali Nie, Shuai Liu
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Abstract

ABSTRACT In forest resource inventory, tree height is often estimated by easily measurable diameter from height-diameter model. In this study, we tried to use random forest (RF), an important machine learning method, to model individual-tree height. Results showed that the optimized RF model had better fitting and prediction accuracy (R 2 = 0.8146 and RMSE = 2.2527 m). In terms of relative importance, diameter at breast height (DBH) was the most important factor, followed by neighborhood-related variables and other variables related to environmental conditions. Further, tree height was generally positively affected by DBH, mean diameter of neighbors, DBH dominance, number of neighbors, and mean annual precipitation, but negatively affected by elevation. The results indicated that the RF-based height model was statistically reliable and highly accurate, and it had strong interpretability with ecological significance. Our study will provide a new perspective for the application of machine learning algorithms to forest dynamic modeling.
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结合邻域效应和环境效应,利用随机森林回归模型模拟单树高度
摘要在森林资源清查中,树木高度通常是通过高度-直径模型中易于测量的直径来估计的。在这项研究中,我们试图使用随机森林(RF)这一重要的机器学习方法来对个体树高进行建模。结果表明,优化后的RF模型具有较好的拟合和预测精度(R2 = 0.8146和RMSE = 2.2527 m) 。就相对重要性而言,胸围(DBH)是最重要的因素,其次是邻域相关变量和其他与环境条件相关的变量。此外,树高通常受到DBH、邻居平均直径、DBH优势度、邻居数量和年平均降水量的正向影响,但受到海拔的负向影响。结果表明,基于RF的身高模型在统计上可靠、准确,具有较强的可解释性和生态学意义。我们的研究将为机器学习算法在森林动态建模中的应用提供一个新的视角。
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来源期刊
CiteScore
3.00
自引率
5.60%
发文量
26
审稿时长
3.3 months
期刊介绍: The Scandinavian Journal of Forest Research is a leading international research journal with a focus on forests and forestry in boreal and temperate regions worldwide.
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