广西师范大学学报（自然科学版）2026，Vol.44Issue(4)：234-245,12.DOI:10.16088/j.issn.1001-6600.2025042601
基于特征选择和集成机器学习算法的森林地上生物量估测
Forest aboveground biomass estimation based on feature selection and ensemble machine learning algorithms
摘要
Abstract
The increasing dimensionality of feature variables in remote sensing-based estimation of forest aboveground biomass(AGB)necessitates effective feature selection to enhance model accuracy.This study focuses on Nanning City as the research area,utilizing Sentinel-2 data as the remote sensing source.Spectral information from various bands,texture features,and additional factors such as elevation,slope,and aspect were extracted.Three feature selection methods including stepwise regression,bivariate correlation,and random forest were employed to identify modeling variables.Biomass estimation models were established based on CatBoost and random forest(RF)machine learning algorithms.Five-fold cross-validation was applied to evaluate model performance,and the best model was used to complete biomass mapping.The results indicated that among the three feature selection methods,the bivariate correlation method performed the best across three tree types:pine,eucalyptus,and broadleaf species.For Chinese fir forests,the random forest method showed superior performance.Specifically:For Chinese fir forests,the combination of the random forest feature selection method and the RF algorithm was optimal(R2=0.58,RMSE=8.53 M g·hm-2).For Masson pine forests,the bivariate correlation method combined with the RF algorithm was the best choice(R2=0.51,RMSE=11.10 Mg·hm-2).For eucalyptus forests,the bivariate correlation method combined with the RF algorithm yielded the best results(R2=0.56,RMSE=14.91 Mg·hm-2).For broadleaf forests,the bivariate correlation method combined with the RF algorithm also proved optimal(R2=0.35,RMSE=40.55 Mg·hm-2).Feature selection methods have a significant impact on the predictive performance of models.Combining feature selection methods with ensemble machine learning algorithms is conducive to improving the estimation accuracy of AGB.关键词
特征选择/随机森林/CatBoost/森林地上生物量/机器学习Key words
feature selection/random forest/CatBoost/forest aboveground biomass/machine learning分类
农业科技引用本文复制引用
罗蜜,邓子蒨,赵学松,吕华权,莫晓峰,吴宇,周伟..基于特征选择和集成机器学习算法的森林地上生物量估测[J].广西师范大学学报（自然科学版）,2026,44(4):234-245,12.基金项目
自然资源部中国—东盟卫星遥感应用重点实验室开放基金(KLCARS-2024-G06) （KLCARS-2024-G06）
广西科技基地和人才专项(桂科 AD23026073) （桂科 AD23026073）
大学生创新创业训练计划(202410603025) （202410603025）
