Earticle

초록

영어

In this study, we sought to compare and evaluate the accuracy and predictive performance of machine learning algorithms for estimating the growth of individual Larix kaempferi trees in Gangwon Province, Korea. We employed linear regression, random forest, XGBoost, and LightGBM algorithms to predict tree growth using monitoring data organized based on different thinning intensities. Furthermore, we compared and evaluated the goodness-of-fit of these models using metrics such as the coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE). The results revealed that XGBoost provided the highest goodness-of-fit, with an R2 value of 0.62 across all thinning intensities, while also yielding the lowest values for MAE and RMSE, thereby indicating the best model fit. When predicting the growth volume of individual trees after 3 years using the XGBoost model, the agreement was exceptionally high, reaching approximately 97% for all stand sites in accordance with the different thinning intensities. Notably, in non-thinned plots, the predicted volumes were approximately 2.1 m3 lower than the actual volumes; however, the agreement remained highly accurate at approximately 99.5%. These findings will contribute to the development of growth prediction models for individual trees using machine learning algorithms.

목차

Abstract
Introduction
Materials and Methods
Research data
Research methods
Results and Discussion
Status of L. kaempferi monitoring data based on thinning intensities
Learning and accuracy evaluation of growth prediction algorithms
Estimation and comparison of forest tree volumes via machine learning algorithms
Conclusion
Acknowledgements
References

키워드

저자

• Hyo-Bin Ji [ Division of Forest Sciences, Department of Forest Management, Kangwon National University, Chuncheon 24345, Republic of Korea ]
• Jin-Woo Park [ Division of Forest Sciences, Department of Forest Management, Kangwon National University, Chuncheon 24345, Republic of Korea ] Corresponding Author
• Jung-Kee Choi [ Division of Forest Sciences, Department of Forest Management, Kangwon National University, Chuncheon 24345, Republic of Korea ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  Journal of Forest and Environmental Science [산림과학연구]

• 간기

  계간

• pISSN

  2288-9744

• eISSN

  2288-9752

• 수록기간

  1981~2025

• 등재여부

  KCI 등재

• 십진분류

  KDC 526 DDC 634