Prediction of aboveground grassland biomass on the Loess Plateau, China, using a random forest algorithm

Abstract 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 sp...

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Autores principales: Yinyin Wang, Gaolin Wu, Lei Deng, Zhuangsheng Tang, Kaibo Wang, Wenyi Sun, Zhouping Shangguan
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Lenguaje:EN
Publicado: Nature Portfolio 2017
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Acceso en línea:https://doaj.org/article/2dd672d16bd04a47aba7689e977e614d
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spelling oai:doaj.org-article:2dd672d16bd04a47aba7689e977e614d2021-12-02T12:32:30ZPrediction of aboveground grassland biomass on the Loess Plateau, China, using a random forest algorithm10.1038/s41598-017-07197-62045-2322https://doaj.org/article/2dd672d16bd04a47aba7689e977e614d2017-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-07197-6https://doaj.org/toc/2045-2322Abstract 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.Yinyin WangGaolin WuLei DengZhuangsheng TangKaibo WangWenyi SunZhouping ShangguanNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-10 (2017)
institution DOAJ
collection DOAJ
language EN
topic Medicine
R
Science
Q
spellingShingle Medicine
R
Science
Q
Yinyin Wang
Gaolin Wu
Lei Deng
Zhuangsheng Tang
Kaibo Wang
Wenyi Sun
Zhouping Shangguan
Prediction of aboveground grassland biomass on the Loess Plateau, China, using a random forest algorithm
description Abstract 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.
format article
author Yinyin Wang
Gaolin Wu
Lei Deng
Zhuangsheng Tang
Kaibo Wang
Wenyi Sun
Zhouping Shangguan
author_facet Yinyin Wang
Gaolin Wu
Lei Deng
Zhuangsheng Tang
Kaibo Wang
Wenyi Sun
Zhouping Shangguan
author_sort Yinyin Wang
title Prediction of aboveground grassland biomass on the Loess Plateau, China, using a random forest algorithm
title_short Prediction of aboveground grassland biomass on the Loess Plateau, China, using a random forest algorithm
title_full Prediction of aboveground grassland biomass on the Loess Plateau, China, using a random forest algorithm
title_fullStr Prediction of aboveground grassland biomass on the Loess Plateau, China, using a random forest algorithm
title_full_unstemmed Prediction of aboveground grassland biomass on the Loess Plateau, China, using a random forest algorithm
title_sort prediction of aboveground grassland biomass on the loess plateau, china, using a random forest algorithm
publisher Nature Portfolio
publishDate 2017
url https://doaj.org/article/2dd672d16bd04a47aba7689e977e614d
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