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Disentangling the Factors That Contribute to the Growth of Betula spp. and Cunninghami lanceolata in China Based on Machine Learning Algorithms

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  • Hanyue Zhang

    (Precision Forestry Key Laboratory of Beijing, Beijing Forestry University, Beijing 100083, China)

  • Zhongke Feng

    (Precision Forestry Key Laboratory of Beijing, Beijing Forestry University, Beijing 100083, China
    Key Laboratory of Genetics and Germplasm Innovation of Tropical Special Forest Trees and Ornamental Plants, Ministry of Education, College of Forestry, Hainan University, Haikou 570228, China
    Intelligent Forestry Key Laboratory of Haikou City, College of Forestry, Hainan University, Haikou 570228, China)

  • Shan Wang

    (Precision Forestry Key Laboratory of Beijing, Beijing Forestry University, Beijing 100083, China)

  • Wenxu Ji

    (Precision Forestry Key Laboratory of Beijing, Beijing Forestry University, Beijing 100083, China)

Abstract

Forests are indispensable materials and spiritual foundations for promoting ecosystem circulation and human survival. Exploring the environmental impact mechanism on individual-tree growth is of great significance. In this study, the effects of biogeoclimate, competition, and topography on the growth of Betula spp. and Cunninghamia lanceolata (Lamb.) Hook., two tree species with high importance value in China, were explored by gradient boosting regression tree (GBRT), k-nearest neighbor (KNN), and random forest (RF) machine learning (ML) algorithms. The results showed that the accuracy of RF was better than KNN, which was better than GBRT. All ML algorithms performed well for future diameter at breast height (DBH) predictions; the Willmott’s indexes of agreement (WIA) of each ML algorithm in predicting the future DBH were all higher than 0.97, and the R 2 was higher than 0.98 and 0.90, respectively. The individual tree annual growth rate is mainly affected by the single-tree size, and the external environment can promote or inhibit tree growth. Climate and stand structure variables were relatively more important for tree growth than the topographic factors. Lower temperature and precipitation, higher stand density, and canopy closure were more unfavorable for their growth. In afforestation, the following factors should be considered in order: geographic location, meteorological climate, stand structure, and topography.

Suggested Citation

  • Hanyue Zhang & Zhongke Feng & Shan Wang & Wenxu Ji, 2022. "Disentangling the Factors That Contribute to the Growth of Betula spp. and Cunninghami lanceolata in China Based on Machine Learning Algorithms," Sustainability, MDPI, vol. 14(14), pages 1-15, July.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:14:p:8346-:d:858097
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    References listed on IDEAS

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    1. Thurnher, Christopher & Klopf, Mario & Hasenauer, Hubert, 2017. "MOSES – A tree growth simulator for modelling stand response in Central Europe," Ecological Modelling, Elsevier, vol. 352(C), pages 58-76.
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    Cited by:

    1. Yilin Zhao & Feng He & Ying Feng, 2022. "Research on the Current Situation of Employment Mobility and Retention Rate Predictions of “Double First-Class” University Graduates Based on the Random Forest and BP Neural Network Models," Sustainability, MDPI, vol. 14(14), pages 1-22, July.
    2. Ismet Yener & Engin Guvendi, 2023. "Predicting and Mapping Dominant Height of Oriental Beech Stands Using Environmental Variables in Sinop, Northern Turkey," Sustainability, MDPI, vol. 15(19), pages 1-20, October.

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