Individual Tree Diameter Growth Models of Larch-Spruce-Fir Mixed Forests Based on Machine Learning Algorithms
Individual Tree Diameter Growth Models of Larch-Spruce-Fir Mixed Forests Based on Machine Learning Algorithms
复制标题
基于机器学习算法的落叶松-云杉-冷杉混交林单树直径生长模型
DOI：
10.3390/f10020187
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发表时间：
2019-02-01
期刊：
影响因子：
2.9
通讯作者：
Shen, Chenchen
中科院分区：
文献类型：
--
作者：
Ou, Qiangxin;Lei, Xiangdong;Shen, Chenchen
单木生长模型是灵活的，通常用于代表异质性和结构复杂的异龄林分的生长动态。除了传统的统计模型外，随机森林（RF）、提升回归树（BRT）、立体主义（Cubist）和多元自适应回归样条（MARS）等非参数和非线性机器学习方法的快速发展为单木生长预测提供了新的途径。然而，这些方法的应用到单木生长建模仍然是有限的，他们的性能比较短。比较和评价了RF、BRT、Cubist和MARS模型在模拟中国东北地区落叶松云冷杉混交林单木直径生长时的性能。总共使用了16，619个来自长期样地的观测结果。基于10倍交叉验证，我们发现RF，BRT和立体主义模型有一个明显的优势，在预测单木直径生长的MARS模型。Cubist模型在模型性能方面排名最高（RMSEcv [0.1351 cm]，MAE（cv）[0.0972 cm]和R-cv（2）[0.5734]），其次是BRT和RF模型，而MARS排名最低（RMSEcv [0.1462 cm]，MAE（cv）[0.1086 cm]和R-cv（2）[0.4993]）。从RF和BRT模型中确定的预测因子的相对重要性表明，竞争和树木大小是直径生长的主要驱动力，气候在局部尺度上解释树木直径生长变化的能力有限。一般而言，RF、BRT和Cubist模型是预测单木直径生长的有效和强大的建模方法。
Individual tree growth models are flexible and commonly used to represent growth dynamics for heterogeneous and structurally complex uneven-aged stands. Besides traditional statistical models, the rapid development of nonparametric and nonlinear machine learning methods, such as random forest (RF), boosted regression tree (BRT), cubist (Cubist) and multivariate adaptive regression splines (MARS), provides a new way for predicting individual tree growth. However, the application of these approaches to individual tree growth modelling is still limited and short of a comparison of their performance. The objectives of this study were to compare and evaluate the performance of the RF, BRT, Cubist and MARS models for modelling the individual tree diameter growth based on tree size, competition, site condition and climate factors for larch-spruce-fir mixed forests in northeast China. Totally, 16,619 observations from long-term sample plots were used. Based on tenfold cross-validation, we found that the RF, BRT and Cubist models had a distinct advantage over the MARS model in predicting individual tree diameter growth. The Cubist model ranked the highest in terms of model performance (RMSEcv [0.1351 cm], MAE(cv) [0.0972 cm] and R-cv(2) [0.5734]), followed by BRT and RF models, whereas the MARS ranked the lowest (RMSEcv [0.1462 cm], MAE(cv) [0.1086 cm] and R-cv(2) [0.4993]). Relative importance of predictors determined from the RF and BRT models demonstrated that the competition and tree size were the main drivers to diameter growth, and climate had limited capacity in explaining the variation in tree diameter growth at local scale. In general, the RF, BRT and Cubist models are effective and powerful modelling methods for predicting the individual tree diameter growth.