Abstract
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], MAEcv [0.0972 cm] and R2cv [0.5734]), followed by BRT and RF models, whereas the MARS ranked the lowest (RMSEcv [0.1462 cm], MAEcv [0.1086 cm] and R2cv [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.
Highlights
Forest growth models are important tools in providing quantitative and reliable information for forest management decisions making [1,2]
Individual Tree Diameter Prediction Based on machine learning (ML) Algorithms
Based on tenfold cross-validation resampling, we found that the random forest (RF), boosted regression tree (BRT) and Cubist models had a distinct advantage over multivariate adaptive regression splines (MARS) in predicting individual tree diameter growth
Summary
Forest growth models are important tools in providing quantitative and reliable information for forest management decisions making [1,2]. These models can be developed with different resolutions including stand, diameter class and individual tree levels [3]. Tree growth is often expressed as a function of tree size, competition and site condition [5,6,7,8,9] Besides these aforementioned factors, climate factors such as temperature, light and precipitation are not negligible in determining tree growth [10]. The climate, site condition and competition interactively influence individual tree growth These influences may vary with tree species and size. Information about contributions of different factors to individual tree growth in mixed forests is still insufficient, and it is necessary to identify the dominant factors affecting tree growth with appropriate methods [14]
Sign-in/Register to view highlights & summary 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.
