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大兴安岭白桦林凋落物载量动态预测

蔡志勇 孙龙 褚腾飞 胡海清

东北林业大学学报2026,Vol.54Issue(10):57-62,71,7.
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东北林业大学学报2026,Vol.54Issue(10):57-62,71,7.

大兴安岭白桦林凋落物载量动态预测

Prediction of Litter Load Dynamics of Betula platyphylla Forests in the Greater Khingan Mountains

蔡志勇 1孙龙 2褚腾飞 3胡海清2

作者信息

  • 1. 东北林业大学林学院,哈尔滨,150040||黑龙江省森林保护研究所,哈尔滨,150081
  • 2. 东北林业大学林学院,哈尔滨,150040
  • 3. 大兴安岭林业集团公司,黑龙江省·加格达奇,165000
  • 折叠

摘要

Abstract

Standing forest litter is regarded as the residual cumulative mass of litter decomposed over successive years.The Olson exponential decay model was adopted to establish a system of simultaneous equations describing the dynamic balance be-tween litter production and litter decomposition.Multiple linear regression was used to fit and solve the parameters of the equations.After simplification and mathematical derivation,litter production and litter decomposition rates were obtained,based on which a prediction model for litter load was constructed.Field measured data from sample plots were used to veri-fy the accuracy of the model.Taking Betula platyphylla,a widely distributed deciduous broad-leaved tree species in the Greater Khingan forest region of Heilongjiang Province,as the research object,the established model was applied to pre-dict and analyze the litter load of sample plots.The model verification results showed that the relative error of predicted lit-ter load in birch sample plots in the next two years ranges from 0.01 to 0.17,with an average relative error of 0.10,indi-cating favorable predictive accuracy of the model.This model can reveal the interaction mechanism among litter produc-tion,litter decomposition rate and litter load,characterize the residual accumulation process of litter,and effectively pre-dict the dynamic variation trend of forest litter load.

关键词

凋落物产量/凋落物分解/Olson模型/森林可燃物/可燃物载量

Key words

Litter production/Litter decomposition/Olson model/Forest fuel/Fuel loading

分类

农业科技

引用本文复制引用

蔡志勇,孙龙,褚腾飞,胡海清..大兴安岭白桦林凋落物载量动态预测[J].东北林业大学学报,2026,54(10):57-62,71,7.

基金项目

黑龙江省省属科研院所科研业务项目(LKSB2025-2) (LKSB2025-2)

国家重点研发计划项目(2023YFC3006800). (2023YFC3006800)

东北林业大学学报

1000-5382

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