北京林业大学学报2026，Vol.48Issue(2)：80-92,13.DOI:10.12171/j.1000-1522.20240389
基于23种多模态数据的生物量和碳储量优化模型构建
Construction of a biomass and carbon storage optimization model based on 23 types of multimodal data:a case study of Cunninghamia lanceolata
摘要
Abstract
[Objective]The accurate estimation of biomass and carbon storage is essential for assessing forest carbon sinks.Constructing an optimization model based on environmental factors can provide technical support for estimating tree biomass and carbon storage at the regional scale.Chinese fir,due to its typical coupling with the site environment,serves as an exemplary case.Existing models often encounter challenges such as limited accuracy due to site-specific variations and inadequate multi-factor coupling modeling and verification.This study intends to construct a scalable model framework,taking Chinese fir as a case study to perform modeling verification,thereby providing a reference for developing models for similar tree species.[Method]In this paper,measured biomass data of Chinese fir across the country were collected through five categories of 23 multimodal data(topography,climate,soil,location and forest variables).Parametric models(basic model,passive variable model,and additive biomass parameter model)and non-parametric models(GA-BP,PSO-BP and BP models)were constructed.The SHAP interpreter was employed to analyze the contribution and interaction effects of each factor.Dummy variables were introduced to evaluate the performance of different models in terms of accuracy enhancement and usability.[Result]The key findings were as follows:(1)The primary factors influencing Chinese fir biomass included four forest variables:diameter at breast height,tree height,forest age and stand density.Secondary factors were longitude and the maximum temperature during the growing season.Soil variables exhibit a relatively minor influence.Physical quantities had the strongest correlation and the highest contribution with variables such as diameter at breast height,tree height,forest age and stand density.The influence of longitude and the maximum temperature of the growing season were secondary,while the contribution of soil variables was relatively low.(2)Overall,except for leaf biomass,the PSO-BP model demonstrated a higher degree of accuracy improvement in biomass estimation.The SHAP interpreter results indicated that in non-parametric biomass models,the contribution of forest tree variables was the highest,and the response of the interaction factor was obvious.(3)The comparison results of verification indicators revealed that the introduction of dummy variables significantly improved the accuracy of the Chinese fir biomass model.[Conclusion]Both parametric and non-parametric models established in this study can provide more accurate biomass simulation for Chinese fir trees.When combined with carbon content parameters,these models can accurately calculate the biomass and carbon storage of various components of Chinese fir trees(whole plant,above ground,below ground,trunk,crown,branches,leaves).The model and environmental variables in this paper have been embedded in an online cloud platform,and can be used online to calculate biomass.关键词
多模态数据/机器学习/生物量模型/碳储量模型/杉木Key words
multimodal data/machine learning/biomass model/carbon storage model/Cunninghamia anceolata分类
农业科技引用本文复制引用
易扬,史明昌,杨宇璐,雷章,马慧莹..基于23种多模态数据的生物量和碳储量优化模型构建[J].北京林业大学学报,2026,48(2):80-92,13.基金项目
上海市自然科学基金面上项目(23ZR1459700),上海市青年科技英才杨帆计划(22YF1444000),上海市软科学研究青年项目(23692120300),上海市绿化和市容管理局项目(G260303). （23ZR1459700）
