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Quantifying the Effects of Stand and Climate Variables on Biomass of Larch Plantations Using Random Forests and National Forest Inventory Data in North and Northeast China

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  • Xiao He

    (Key Laboratory of Forest Management and Growth Modelling, National Forestry and Grassland Administration, Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China)

  • Xiangdong Lei

    (Key Laboratory of Forest Management and Growth Modelling, National Forestry and Grassland Administration, Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China)

  • Weisheng Zeng

    (Academy of Inventory and Planning, National Forestry and Grassland Administration, Beijing 100714, China)

  • Linyan Feng

    (Key Laboratory of Forest Management and Growth Modelling, National Forestry and Grassland Administration, Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China)

  • Chaofan Zhou

    (Key Laboratory of Forest Management and Growth Modelling, National Forestry and Grassland Administration, Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China)

  • Biyun Wu

    (Key Laboratory of Forest Management and Growth Modelling, National Forestry and Grassland Administration, Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China)

Abstract

The accurate estimation of forest biomass is crucial for supporting climate change mitigation efforts such as sustainable forest management. Although traditional regression models have been widely used to link stand biomass with biotic and abiotic predictors, this approach has several disadvantages, including the difficulty in dealing with data autocorrelation, model selection, and convergence. While machine learning can overcome these challenges, the application remains limited, particularly at a large scale with consideration of climate variables. This study used the random forests (RF) algorithm to estimate stand aboveground biomass (AGB) and total biomass (TB) of larch ( Larix spp.) plantations in north and northeast China and quantified the contributions of different predictors. The data for modelling biomass were collected from 445 sample plots of the National Forest Inventory (NFI). A total of 22 independent variables (6 stand and 16 climate variables) were used to develop and train climate-sensitive stand biomass models. Optimization of hyper parameters was implemented using grid search and 10-fold cross-validation. The coefficient of determination ( R 2 ) and root mean square error ( RMSE ) of the RF models were 0.9845 and 3.8008 t ha −1 for AGB, and 0.9836 and 5.1963 t ha −1 for TB. The cumulative contributions of stand and climate factors to stand biomass were >98% and <2%, respectively. The most crucial stand and climate variables were stand volume and annual heat-moisture index (AHM), with relative importance values of >60% and ~0.25%, respectively. The partial dependence plots illustrated the complicated relationships between climate factors and stand biomass. This study illustrated the power of RF for estimating stand biomass and understanding the effects of stand and climate factors on forest biomass. The application of RF can be useful for mapping of large-scale carbon stock.

Suggested Citation

  • Xiao He & Xiangdong Lei & Weisheng Zeng & Linyan Feng & Chaofan Zhou & Biyun Wu, 2022. "Quantifying the Effects of Stand and Climate Variables on Biomass of Larch Plantations Using Random Forests and National Forest Inventory Data in North and Northeast China," Sustainability, MDPI, vol. 14(9), pages 1-16, May.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:9:p:5580-:d:809439
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    References listed on IDEAS

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    1. Nabiul Islam Khan & Mithun Chandra Shil & Md Salim Azad & Md Nazmus Sadath & S.M. Feroz & Abdus Subhan Mollick, 2018. "Allometric relationships of stem volume and stand level carbon stocks at varying stand density in Swietenia macrophylla King plantations, Bangladesh," ULB Institutional Repository 2013/285094, ULB -- Universite Libre de Bruxelles.
    2. Ph. Ciais & M. Reichstein & N. Viovy & A. Granier & J. Ogée & V. Allard & M. Aubinet & N. Buchmann & Chr. Bernhofer & A. Carrara & F. Chevallier & N. De Noblet & A. D. Friend & P. Friedlingstein & T. , 2005. "Europe-wide reduction in primary productivity caused by the heat and drought in 2003," Nature, Nature, vol. 437(7058), pages 529-533, September.
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    1. Xinghui Wang & Yuman Sun & Weiwei Jia & Hezhi Wang & Wancai Zhu, 2023. "Coupling of Forest Carbon Densities with Landscape Patterns and Climate Change in the Lesser Khingan Mountains, Northeast China," Sustainability, MDPI, vol. 15(20), pages 1-17, October.

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