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

Hanyue Zhang, Zhongke Feng, Shan Wang and Wenxu Ji
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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
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

Sustainability, 2022, vol. 14, issue 14, 1-15

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.

Keywords: gradient boosting regression tree; k-nearest neighbor; random forest; environmental impact mechanism; individual-tree annual growth rate; future DBH prediction (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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