Comparing the random forest algorithm with other modelling approaches to capture the complex patterns of intra-annual wood formation of Chinese fir with different ages

Jiang, Yanyan; Zhang, Xiongqing; Zhang, Jianguo; Chhin, Sophan

doi:10.1016/j.dendro.2022.126043

Cited by 3 publications

(1 citation statement)

References 49 publications

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“…Wang et al [24] used a random forest algorithm to explore the relationship between forest productivity and stand and climate factors. Jiang et al [25] modeled the intra-annual wood formation dynamics of a fir forest with central subtropical climate characteristics by using a random forest algorithm. Yang et al [26] used a mixed-effects random forest algorithm to describe the height-diameter relationship of Caribbean trees.…”

Section: Introductionmentioning

confidence: 99%

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

Gao,

Dong,

Gong

et al. 2023

Sustainability

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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 R2 of the site index model results was 0.581, and the R2 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.

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Section: Introductionmentioning

confidence: 99%

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

Gao,

Dong,

Gong

et al. 2023

Sustainability

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Enhancing forest insect outbreak detection by integrating tree-ring and climate variables

Jiang,

Wang,

Zhang

et al. 2024

J. For. Res.

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Geographical origin classification of Phoebe zhennan and Phoebe bournei by solid phase micro-extraction and gas chromatography-mass spectrometry

et al. 2023

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Phoebe zhennan and Phoebe bournei are favored for their fine-grain, insect resistance, special fragrance, not easily cracked and deformed, which had been widely used in furniture and construction. Wood authenticity regarding different varieties and geographical origins is increasingly becoming a concern for consumers. Due to the difference in wood quality characteristics and economic value of genus Phoebe species from different geographical origins, it is important to establish a fast, efficient, and reliable method to discriminate the geographical origin of Phoebe zhennan and Phoebe bournei. Solid phase micro-extraction (SPME) was used as sample preparation approach and gas chromatography-mass spectrometry (GC–MS) technique was employed to analyze the volatile compounds. As a result, a total of 40 volatiles were identified by SPME–GC–MS among Phoebe zhennan samples from four origins, while 34 substances identified among Phoebe bournei samples. The wood samples of Phoebe zhennan and Phoebe bournei from different major production areas in China were able be discriminated by GC–MS spectroscopy. This finding indicated that genus Phoebe species from different climate zones had different chemical composition, which verified that the SPME–GC–MS technique can be used to classify different species of wood with characteristic compounds.

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Comparing the random forest algorithm with other modelling approaches to capture the complex patterns of intra-annual wood formation of Chinese fir with different ages

Cited by 3 publications

References 49 publications

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

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

Enhancing forest insect outbreak detection by integrating tree-ring and climate variables

Geographical origin classification of Phoebe zhennan and Phoebe bournei by solid phase micro-extraction and gas chromatography-mass spectrometry

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