Abstract
Improving prediction accuracy is a prominent modeling issue in relation to forest simulations, and ensemble learning is a new effective method for improving the precision of crown profile model simulations in order to overcome the disadvantages of statistical modeling. Background: Ensemble learning (a machine learning paradigm in which multiple learners are trained to achieve better performance) has strong nonlinear problem learning ability and flexibility in terms of analyzing longitudinal data, and it remains rarely explored so far in the field of crown profile modeling forest science. In this study, we explored the application of ensemble learning to the modeling and prediction of crown profiles. Methods: We evaluated the performance of ensemble learning procedures and marginal model in modeling crown profile using the crown profile database from China fir plantations in Fujian, in southern China. Results: The ensemble learning approach for the crown profile model appeared to have better performance and higher efficiency (R2 > 0.9). The crown equation model 18 showed an intermediate performance in its estimation, whereas GBDT (MAE = 0.3250, MSE = 0.2450) appeared to have the best performance and higher efficiency. Conclusions: The ensemble learning method can combine the advantages of multiple learners and has higher model accuracy, robustness and overall induction ability, and is thus an effective technique for crown profile modeling and prediction.
Highlights
Publisher’s Note: MDPI stays neutralWith the increasingly frequent need to meet multi-resource objectives in plantation forestry, forest growth and yield modeling has increasingly focused on modeling individual trees [1]
The test results showed that the random forest (RF), AdaBoost, gradient boosting decision tree (GBDT) and XGBoost had no over-fitting phenomenon; Multi-Layer Perceptron (MLP) and support vector regression (SVR)
Based on an analysis of the disadvantages of crown profile modeling, in this study we developed two promising modeling methods, which represent new and essential explorations in the study of parametric and nonparametric methods
Summary
With the increasingly frequent need to meet multi-resource objectives in plantation forestry, forest growth and yield modeling has increasingly focused on modeling individual trees [1]. Crown size and crown dimensions are important variables for imparting biological realism to individual-tree growth models [2]. The crown profile (crown width at any point in the crown [3]) affects the tree’s physiological processes, principally photosynthesis, respiration, and transpiration, due to the utilization of light and precipitation, reflecting the crown size and crown dimensions [4–6]. Crown size is commonly used as both a predictor variable and a response variable in forest growth and yield models and biomass models [2,7–11]. Crown dimensions are useful in modeling individual tree forms [12–21], characterizing stand density [7,10,22], predicting subject tree growth [4,23,24], providing insights into various ecophysiological processes [25,26], and portraying competition among neighboring trees [24,27–30].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
