Development of climate-sensitive nonlinear mixed-effects tree height-DBH model for <i>Cunninghamia lanceolata</i>

Du Zhi; Chen Zhenxiong; Li Rui; Liu Ziwei; Huang Xin

doi:10.12171/j.1000-1522.20230052

Journal of Beijing Forestry University > 2023 > 45(9): 52-61. > DOI: 10.12171/j.1000-1522.20230052

Du Zhi, Chen Zhenxiong, Li Rui, Liu Ziwei, Huang Xin. Development of climate-sensitive nonlinear mixed-effects tree height-DBH model for Cunninghamia lanceolata[J]. Journal of Beijing Forestry University, 2023, 45(9): 52-61. DOI: 10.12171/j.1000-1522.20230052

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Development of climate-sensitive nonlinear mixed-effects tree height-DBH model for Cunninghamia lanceolata

Central South Academy of Inventory and Planning of National Forestry and Grassland Administration, Changsha 410014, Hunan, China

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Received Date: March 09, 2023
Revised Date: April 11, 2023
Accepted Date: May 11, 2023
Available Online: May 14, 2023
Published Date: September 24, 2023

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Abstract

Abstract

Objective The nonlinear mixed-effects tree height-DBH model of Cunninghamia lanceolata based on stand dominant height and climate factors is established, which provides theoretical basis for the research on growth and forest management.
Method Based on the annual monitoring and evaluation of national forest resources of Guangxi Zhuang Autonomous Region, southern China in 2020, this study used the data of DBH and height of each tree, climate data of 25 Cunninghamia lanceolata sample plots, chose the basic model with the highest simulation accuracy among seven common height-DBH models. On this basis, stand competition, site condition and climatic factors were used to build generalized nonlinear model, then used the sample plot effect to build nonlinear mixed-effects model. The 10-fold cross-validation method was applied to the test of three models.
Result Chapman-Richards model was the basic height-DBH model with the highest accuracy. The stand dominant height, basal area of forest stands and the mean annual precipitation were significantly related to the tree height growth, which were used to build generalized nonlinear model. Through comparative analysis, the study selected three random parameters to build nonlinear mixed-effects model. The adjustment determination coefficient of basic model, generalized nonlinear model and nonlinear mixed-effects model were 0.674 2, 0.797 3 and 0.857 3, respectively, the mean absolute errors were 1.607 5, 1.270 1 and 1.010 6 m, the root-mean-square errors were 2.032 1, 1.632 1 and 1.338 4 m, and the relative root mean square errors were 20.796 4%, 16.703 3% and 13.697 3%, respectively. The nonlinear mixed-effects model showed the best fitting effect.
Conclusion Using nonlinear mixed-effects tree height-DBH model based on stand dominant height and climatic factors can better describe the height-DBH curve of Cunninghamia lanceolata, which is suitable for the prediction of tree height on a large scale.
- Cunninghamia lanceolata,
- dominant height,
- climatic factor,
- tree height-DBH model,
- nonlinear mixed-effect model

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References

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Development of climate-sensitive nonlinear mixed-effects tree height-DBH model for Cunninghamia lanceolata

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