Developing an optimal individual tree diameter growth model for uneven-aged Pinus yunnanensis forests using machine learning algorithms

The objective of this study was to develop more accurate predictions of the diameter growth of Pinus yunnanensis and to analyze the impact of various factors on its diameter growth, providing valuable management recommendations for forest management. To this end, various machine learning methods wer...

Full description

Saved in:
Bibliographic Details
Published in:Trees (Berlin, West) Vol. 39; no. 4; p. 58
Main Authors: Deng, Longfeng, Wang, JianMing, Yin, JiTing, Guan, YaDong
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2025
Springer Nature B.V
Subjects:
Agriculture
Biomedical and Life Sciences
China
climate
Core analysis
Forest management
Forestry
Growth models
Impact analysis
Learning algorithms
Life Sciences
Machine learning
Multilayer perceptrons
neural networks
Original Article
Pinus yunnanensis
Plant Anatomy/Development
Plant Pathology
Plant Physiology
Plant Sciences
surveys
tree and stand measurements
tree growth
Trees
China
Multilayer perceptron and its stacking integration algorithm
Random forest
XGBoost
Diameter growth
ISSN:0931-1890, 1432-2285
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The objective of this study was to develop more accurate predictions of the diameter growth of Pinus yunnanensis and to analyze the impact of various factors on its diameter growth, providing valuable management recommendations for forest management. To this end, various machine learning methods were employed to construct individual tree diameter growth models for P . yunnanensis . The research was based on single-period survey data and core sample data from 11 permanent plots in Cangshan mountain, Dali, Yunnan Province. In addition, the impacts of tree size, competition, site quality, and climatic factors on the growth of P. yunnanensis diameters were considered. Four machine learning methods were employed to develop the models: Random Forest, XGBoost, Multilayer Perceptron, and Stacked Multilayer Perceptron (Stacked-MLP). The models were evaluated and compared using a k-fold strategy, based on the coefficient of determination, Root Mean Square Error, and Mean Absolute Error. The results of the fivefold cross-validation demonstrated that the Stacked-MLP model exhibited the highest performance, with an R2 of 0.8508, RMSE of 0.2907 cm 2 , and MAE of 0.1928 cm 2 . The feature importance methods from Random Forest, XGBoost, and SHAP analysis indicated that competition and tree size were the primary drivers of tree growth, while climate and site factors had a more limited impact in explaining variations in tree growth on a small, local scale.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:0931-1890
1432-2285
DOI:10.1007/s00468-025-02634-w