西南林业大学学报2026，Vol.46Issue(3)：160-167,8.DOI:10.11929/j.swfu.202409052
基于不同机器学习分类算法的滇西北森林碳储量估测
The Estimation of Forest Carbon Storage in Northwest Yunnan Based on Different Machine Learning Classification Algorithms
摘要
Abstract
Taking northwest Yunnan as the study area,this research utilizes the Google Earth Engine(GEE)platform and applies 4 machine learning algorithms—Random Forest(RF),Support Vector Machine(SVM),De-cision Tree(DT),and K-Nearest Neighbors(KNN)—to classify the region into 5 land cover types:forest land,cropland,built-up land,water bodies,and others.By integrating Landsat 8 multispectral remote sensing data and Yunnan Province's forest resource inventory data,23 significantly correlated feature factors were selected,and 5 principal components were extracted through factor analysis.Subsequently,4 regression models—Random Forest Regression,Support Vector Machine Regression,ExtraTrees Regression,and LightGBM Regression—were em-ployed to estimate above-ground forest carbon storage,with the optimal model selected for carbon storage estima-tion.The results indicate that among 4 machine learning classification algorithms,the Random Forest classifier achieved the highest classification accuracy,with an overall accuracy of 0.88 and a Kappa coefficient of 0.84,meeting the requirements for subsequent carbon storage estimation.Regarding the four carbon storage estimation models,the Random Forest Regression model demonstrated the best performance,with an R2 of 0.89,RMSE of 9.22,rRMSE of 15％,and MAE of 2.75,showing strong model fitting capabilities.The total above-ground car-bon storage of forests in northwest Yunnan was estimated at 371.7 Tg,with an average carbon storage of 42.64 t/hm2.Specifically,the carbon storage in Dali Bai Autonomous Prefecture,Diqing Zang Nnationality Autonom-ous Prefecture,Lijiang City,and Nujiang Lisu Autonomous Prefecture was 109.61 Tg,96.7 Tg,86.44 Tg,and 78.94 Tg,respectively.Among the various machine learning algorithms,the Random Forest-based model exhib-ited the highest accuracy for estimating above-ground forest carbon storage in northwest Yunnan.关键词
机器学习/分类/遥感估测/碳储量/滇西北Key words
machine learning/classification/remote sensing estimation/carbon storage/northwest Yunnan分类
农业科技引用本文复制引用
范华鹏,刘畅,程锋云..基于不同机器学习分类算法的滇西北森林碳储量估测[J].西南林业大学学报,2026,46(3):160-167,8.基金项目
云南省兴滇英才支持计划青年拔尖项目(YNWR-QNBJ-2019-064)资助 （YNWR-QNBJ-2019-064）
云南省基础研究计划项目(202401AT070272)资助. （202401AT070272）
