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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
Source PublicationScientific Reports
Volume7Issue:1Pages:6940
Subtype期刊论文
AbstractGrasslands 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
Indexed BySCI
Project Number2016YFC0501605 ; 41390463 ; 2014FY210100
Language英语
Funding OrganizationNational 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
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ieecas.cn/handle/361006/5611
Collection生态环境研究室
Corresponding AuthorShuangguan,Zhouping
Affiliation1.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
Recommended Citation
GB/T 7714
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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