A Method for Estimating Tree Growth Potential with Back Propagation Neural Network

Jianfeng Yao, Cancong Zhao, Xuefan Hu (), Yingshan Jin (), Yanling Li, Liming Cai, Zhuofan Li, Fang Li and Fang Liang
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Jianfeng Yao: College of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China
Cancong Zhao: College of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China
Xuefan Hu: Beijing Key Laboratory of Greening Plants Breeding, Beijing Academy of Forestry and Landscape Architecture, Beijing 100102, China
Yingshan Jin: Beijing Key Laboratory of Greening Plants Breeding, Beijing Academy of Forestry and Landscape Architecture, Beijing 100102, China
Yanling Li: College of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China
Liming Cai: Henan Dabieshan National Field Observation and Research Station of Forest Ecosystem, Zhengzhou 450046, China
Zhuofan Li: Henan Dabieshan National Field Observation and Research Station of Forest Ecosystem, Zhengzhou 450046, China
Fang Li: Beijing Key Laboratory of Greening Plants Breeding, Beijing Academy of Forestry and Landscape Architecture, Beijing 100102, China
Fang Liang: Beijing Key Laboratory of Greening Plants Breeding, Beijing Academy of Forestry and Landscape Architecture, Beijing 100102, China

Sustainability, 2025, vol. 17, issue 4, 1-15

Abstract: Tree growth potential is crucial for maintaining forest health and sustainable development. Traditional expert-based assessments of growth potential are inherently subjective. To address this subjectivity and improve accuracy, this study proposed a method of using Backpropagation Neural network (BPNN) to classify tree growth potential. 60 Pinus tabulaeformis (Carr.) and 60 Platycladus orientalis (Linn.) were selected as experimental trees in the Miyun Reservoir Water Conservation Forest Demonstration Zone in Beijing, and 95 Pinus massoniana (Lamb.) and 60 Cunninghamia lanceolate (Linn.) were selected as experimental trees in the Jigongshan Nature Reserve. The average annual ring width of the outermost 2 cm xylem of the experimental trees were measured by discs or increment cores, and the wood volume increment of each experimental trees in recent years were calculated. According to wood volume increment, the growth potential of experimental trees was divided into three levels: strong, medium, and weak. Using tree height, breast height diameter, average crown width as input variables, using growth potential level as output variables, four sub models for each tree species were established; Using tree species, tree height, breast height diameter, average crown width as input variables, using growth potential level as output variables, a generalized model was established for these four tree species. The test results showed that the accuracy of the sub models for Pinus tabulaeformis , Platycladus orientalis , Pinus massoniana , and Cunninghamia lanceolate were 68.42%, 77.78%, 86.21%, and 78.95%, respectively, and the accuracy of the generalized model was 71.19%. These findings suggested that employing BPNN is a viable approach for accurately estimating tree growth potential.

Keywords: tree growth potential; BPNN; forest management; classification model (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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