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Prediction of abovegroundgrassland biomass on the LoessPlateau, China, using a randomforest algorithm
Wang,YY(Wang,Yinyin)1,2; Wu,GL(Wu,Gaolin)1; Deng,L(Deng,Lin)1; Tang,ZS(Tang,Zhuangsheng)3; Sun,WY(Sun,Wenyi)1; Shuangguan,ZP(Shuangguan,Zhouping)1,2; Shuangguan,Zhouping
2017-07-31
发表期刊Scientific Reports
卷号7期号:1页码:6940
文章类型期刊论文
摘要Grasslands are an important component of terrestrial ecosystems that play a crucial role in the carbon cycle and climate change. In this study, we collected aboveground biomass (AGB) data from 223 grassland quadrats distributed across the Loess Plateau from 2011 to 2013 and predicted the spatial distribution of the grassland AGB at a 100-m resolution from both meteorological station and remote sensing data (TM and MODIS) using a Random Forest (RF) algorithm. The results showed that the predicted grassland AGB on the Loess Plateau decreased from east to west. Vegetation indexes were positively correlated with grassland AGB, and the normalized difference vegetation index (NDVI) acquired from TM data was the most important predictive factor. Tussock and shrub tussock had the highest AGB, and desert steppe had the lowest. Rainfall higher than 400 m might have benefitted the grassland AGB. Compared with those obtained for the bagging, mboost and the support vector machine (SVM) models, higher values for the mean Pearson coefficient (R) and the symmetric index of agreement (λ) were obtained for the RF model, indicating that this RF model could reasonably estimate the grassland AGB (65.01%) on the Loess Plateau.
DOI10.1038/s41598-017-07197-6
收录类别SCI
所属项目编号2016YFC0501605 ; 41390463 ; 2014FY210100
语种英语
项目资助者National Key Research and Development Program of China ; National Key Research and Development Program of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Sci-TechBasic Program of China ; National Sci-TechBasic Program of China
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文献类型期刊论文
条目标识符http://ir.ieecas.cn/handle/361006/5611
专题生态环境研究室
通讯作者Shuangguan,Zhouping
作者单位1.Institute of Soil and Water Conservation
2.University of Chinese Academy of Sciences
3.State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, 712100, Yangling, Shaanxi, P.R. China
4.Institute of Earth Environment
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Wang,YY,Wu,GL,Deng,L,et al. Prediction of abovegroundgrassland biomass on the LoessPlateau, China, using a randomforest algorithm[J]. Scientific Reports,2017,7(1):6940.
APA Wang,YY.,Wu,GL.,Deng,L.,Tang,ZS.,Sun,WY.,...&Shuangguan,Zhouping.(2017).Prediction of abovegroundgrassland biomass on the LoessPlateau, China, using a randomforest algorithm.Scientific Reports,7(1),6940.
MLA Wang,YY,et al."Prediction of abovegroundgrassland biomass on the LoessPlateau, China, using a randomforest algorithm".Scientific Reports 7.1(2017):6940.
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