中南林业科技大学学报2026，Vol.46Issue(6)：140-152,13.DOI:10.14067/j.cnki.1673-923x.2026.06.014
基于机器学习的防护林树种光合调控与碳汇能力评估框架
A machine learning-based framework for evaluating photosynthetic regulation and carbon sequestration capacity among shelter forest species
摘要
Abstract
[Objective]The middle section of the Yinshan mountains in Inner Mongolia,as a representative area of the Three-North Shelter Forest Program,faces critical challenges in enhancing ecosystem carbon sequestration functions.This study aims to investigate the diurnal variation characteristics of photosynthesis and water use efficiency(WUE)in arbor and shrub species in the Middle Section of the Yinshan Mountains,analyze the influence of environmental factors on photosynthetic regulation,and comprehensively evaluate the ecological adaptability and carbon sequestration functions of different tree species.For the first time,a combination of random forest regression models and cluster analysis is applied to provide scientific insights for improving shelter forest ecosystem functions.[Method]Seven arbor species and five shrub species were selected in the middle section of the Yinshan mountains,Inner Mongolia.Photosynthetic parameters were measured using a CI-340 portable photosynthesis system.Spearman's correlation analysis,principal component analysis(PCA),random forest regression modeling,and hierarchical cluster analysis were employed to explore the photosynthetic mechanisms and ecological adaptability of the species.[Result]The study revealed distinct diurnal variation patterns in photosynthetic activity among arbor and shrub species.Net photosynthetic rate(Pn)and WUE exhibited significant interspecific differences,with single-peak and double-peak patterns observed.Temperature,stomatal conductance,and transpiration rate were identified as key regulators of photosynthesis,particularly in coniferous species,which demonstrated higher WUE.Random forest regression analysis highlighted the critical roles of temperature and water management in photosynthetic processes.Cluster analysis further revealed marked differences in carbon sequestration capacity and ecological adaptability among species,offering critical guidance for species selection and carbon sequestration assessments.[Conclusion]This study underscores the significant differences in photosynthesis and WUE between arbor and shrub species in the middle section of the Yinshan mountains.Temperature,stomatal conductance,and transpiration rate were pivotal in photosynthetic regulation.Unlike traditional linear regression methods,the random forest model effectively captured the nonlinear relationships between environmental variables and photosynthetic rates.The hierarchical cluster analysis,applied for the first time in this context,identified distinct functional groups of species based on their carbon sequestration potential and ecological adaptability.These findings fill a critical gap in understanding photosynthetic mechanisms and carbon sequestration capacity within the Three-North Shelter forest program,providing a novel framework for quantitative carbon sequestration assessment and informing future afforestation and restoration strategies.关键词
内蒙古阴山中段/净光合效率/随机森林/固碳释氧/层次聚类分析Key words
the middle section of the Yinshan mountains in Inner Mongolia/net photosynthetic efficiency/random forest/carbon fixation and oxygen release/hierarchical cluster analysis分类
农业科技引用本文复制引用
韩淑敏,宋景良,星星,张瑞,冀鹏浩,高润红,姚瑶,吕整荣,杨俊玲,李峰..基于机器学习的防护林树种光合调控与碳汇能力评估框架[J].中南林业科技大学学报,2026,46(6):140-152,13.基金项目
国家自然科学基金地区科学基金项目(32360249) （32360249）
内蒙古自治区高校青年教师科研创新能力支持项目(BR230161) （BR230161）
包头市森林草原湿地碳汇监测项目(RH2200000501). （RH2200000501）
