Site Quality Evaluation Model of Chinese Fir Plantations for Machine Learning and Site Factors

Weifang Gao, Chen Dong (), Yuhao Gong, Shuai Ma, Jiahui Shen and Shangqin Lin
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Weifang Gao: College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China
Chen Dong: College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China
Yuhao Gong: College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China
Shuai Ma: College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China
Jiahui Shen: College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China
Shangqin Lin: College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China

Sustainability, 2023, vol. 15, issue 21, 1-20

Abstract: Site quality evaluation is an important foundation for decision-making and planning in forest management and provides scientific decision support and guidance for the sustainable development of forests and commercial plantations. Site index and site form models were constructed and subsequently compared utilizing fir ( Cunninghamia lanceolata ) plantations in Nanping City, Fujian Province, China. This papers aim was to construct a site quality classification model, conduct further analysis on the effects of different site factors on the quality of the site, and achieve an assessment of site quality for Chinese fir plantations. An algebraic difference approach was used to establish a site index model and a site form model for Chinese fir in Fujian Province. The suitability of the two models was compared using model accuracy analysis and partial correlation, and the optimal model was chosen for classifying the site quality of the stands. On this basis, a site quality classification model was established using the random forest algorithm, and the importance of each site factor was determined through importance ranking in terms of their impact on site quality. Within the study area, the R 2 of the site index model results was 0.581, and the R 2 values of the five site form models based on different reference breast diameters, ranked from high to low, were 0.894, 0.886, 0.884, 0.880, and 0.865. The bias correlation coefficient between site form and stand volume was 0.71, and the bias correlation coefficient between site index and stand volume was 0.52. The results confirmed that the site form model is better suited for evaluating the site quality of Chinese fir plantations. The random forest-based site form classification model had a high classification accuracy with a generalization accuracy of 0.87. The factors that had the greatest impact on site form were altitude, canopy closure, and slope gradient, whereas landform had the smallest impact on site form. These results can provide a reference for the evaluation of the site quality of plantations and natural forests in southern China to ensure the long-term sustainable use of forest resources.

Keywords: site index; site form; Chinese fir; site quality; random forest algorithm; algebraic difference approach (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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