西北林学院学报2024，Vol.39Issue(1)：10-18,9.DOI:10.3969/j.issn.1001-7461.2024.01.02
基于机器学习算法的森林生物量多源遥感估测
Multi-source Remote Sensing Estimation of Forest Biomass Based on Machine Learning Algorithm
摘要
Abstract
In order to further explore the estimation performance of forest biomass at 10 m×10 m geoscale and the estimation effect of collaborative machine learning algorithms(RF,SVM,DT,GBM,k-NN,Stac-king)of the images with different spatial resolutions,this study used optical remote sensing GF2(Gaofen-2 satellite),sentinel 2A,Landsat 8 OLI,SUM(integration of three remote sensing data sources)images and auxiliary variables DEM elevation data,environmental factors,forest stand factors(forest type,domi-nant tree species).Under the variable selection of Boruta algorithm,the machine learning algorithm was used to estimate the forest biomass(above ground+under ground)in Yuanmou area,and the estimation accuracy under the four images was compared.The results showed that 1)based on the Boruta algorithm,the vegetation index PEIP of sentinel2A,the texture factor of Landsat 8 OLI b2_ME_9 X 9 and the GNDVI of GF2 in a single image were the highest score variables under the three images,and the GNDVI of GF2 in forest biomass was estimated by multi-source fusion as the best score variable.2)Based on the variables selected by the Boruta algorithm,RF,SVM,DT,GBM,k-NN algorithm and stacking integration algorithm of 5 models,SUM's Stacking integration algorithm had the best estimation effect,model R2 was 0.73,RMSE was 28.46 t·hm-2,the estimation performance of SUM under the ensemble algorithm was better than sentinel2A and Landsat 8 OLI,GF2 was better than sentinel2A,sentinel2A outperformed Landsat 8 OLI,indicating that high-resolution images have good estimation effects in biomass remote sensing estima-tion,and multi-source remote sensing collaborative estimation and integrated algorithms can improve the accuracy of forest biomass remote sensing estimation,which can provide references for forest biomass re-mote sensing estimation.关键词
森林生物量遥感估测/多源遥感/机器学习/算法集成/元谋Key words
forest biomass remote sensing estimation/multi-source remote sensing/machine learning/al-gorithm integration/Yuanmou分类
农业科技引用本文复制引用
黄天宝,欧光龙,吴勇,徐熊伟,王振会,蔺如喜,徐灿..基于机器学习算法的森林生物量多源遥感估测[J].西北林学院学报,2024,39(1):10-18,9.基金项目
中国地质调查局昆明自然资源调查中心全国典型地区碳汇综合调查研究(ZD20220133) （ZD20220133）
国家自然科学基金(31770677、31760206、31660202) （31770677、31760206、31660202）
云南省王广兴专家工作站(2018IC100) （2018IC100）
云南省万人计划青年拔尖人才专项(YNWR-QNBJ-2018-184). （YNWR-QNBJ-2018-184）
