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Scientia Silvae Sinicae ›› 2025, Vol. 61 ›› Issue (4): 117-128.doi: 10.11707/j.1001-7488.LYKX20240311

• Research papers • Previous Articles     Next Articles

Additive Biomass Models and Carbon Content of Thirteen Typical Shrubs in Erdos Region

Xia Liu1,2,Chengxing Ling1,2,*(),Yongfu Chen1,2,Hua Liu1,2,Zhenping He3,Zejiang Li4,Weina Sun4,Zhijie Ma3,Haixia You5,Wen Lü6,Feng Zhao1,2,Haowei Zeng1,2,Xinmiao Wang1,2   

  1. 1. Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry　Beijing 100091
    2. Key Laboratory of Forestry Remote Sensing and Information System, National Forestry and Grassland Administration　Beijing 100091
    3. Ordos Forestry and Grassland Bureau　Erdos 017000
    4. Erdos International Desertification Control Technology Innovation Center　Erdos 017000
    5. Erdos Agricultural and Animal Husbandry Technology Extension Center　Erdos 017000
    6. Erdos Vocational College of Ecological Environment　Erdos 017000
  • Received:2024-05-28 Online:2025-04-25 Published:2025-04-21
  • Contact: Chengxing Ling E-mail:lingcx@ifrit.ac.cn

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Abstract:

Objective: This study aims to construct additive biomass models for thirteen typical shrubs in Erdos region based on crown area and shrub height variables, and to determine whole-plant integrated carbon content by weighting biomass allocation coefficients, thereby providing foundational support for precise assessment of shrub carbon storage at regional scales. Method: Targeting thirteen typical shrub species in Erdos, Inner Mongolia, we measured biomass and carbon content of each organ. Four model types, linear models, logarithmic models, power functions, and theoretical growth models, were employed to construct basic shrub biomass models with crown area, shrub height, and crown volume as independent variables. The optimal model forms for each organ’s biomass were then selected, and additive biomass models were constructed using a multivariate nonlinear joint estimation method with component summation. Finally, weighted regression was applied to eliminate model heteroscedasticity. The integrated carbon content of each shrub species was calculated by weighting the carbon content of each organ according to its biomass proportion. Result: For the thirteen shrub species in Erdos, power functions performed best as the basic biomass equations. The constructed additive biomass models achieved high precision, with most models having R2 (coefficient of determination) values generally above 0.8 and normalized mean squared error (NMSE) close to 0.1. Among the single-factor predictors, crown area provided higher model accuracy than shrub height. Composite factors combining crown area and shrub height (e.g., crown volume) were the optimal independent variables for most shrub biomass models. The carbon content of organs showed variability, ranging from 28.86% to 46.97%. The carbon content of the same organ varied significantly among different shrub species. The weighted average carbon content of organs for the thirteen shrubs ranged from 34.68% to 42.37%. Conclusion: Power function models are the best form for predicting shrub biomass. Additive biomass models using composite indicators of crown area and shrub height as independent variables exhibit high precision and practicality. Carbon content varies among organs and whole plants of different shrub species. Therefore, differences in species-specific carbon content must be considered when estimating shrub carbon storage. The results of this study provide parameters and model support for precise remote sensing monitoring and assessment of shrub carbon storage and carbon sinks in arid and semi-arid regions.

Key words: shrub biomass, additive biomass models, carbon content, desert, Erdos

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