Estimating grassland aboveground biomass on the Tibetan Plateau using a random forest algorithm

doi:10.1016/j.ecolind.2019.02.023

	Estimating grassland aboveground biomass on the Tibetan Plateau using a random forest algorithm
	Zeng, Na 1,2; Ren, Xiaoli 1; He, Honglin 1,3; Zhang, Li 1,3; Zhao, Dan 4; Ge, Rong 1,2; Li, Pan 5; Niu, Zhongen 1,2
刊名	ECOLOGICAL INDICATORS
	2019-07-01
卷号	102 页码:479-487
关键词	Aboveground biomass (AGB) The Tibetan Plateau Random Forest (RF) Mean annual temperature (MAT) Mean annual precipitation (MAP)
ISSN号	1470-160X
DOI	10.1016/j.ecolind.2019.02.023
通讯作者	Ren, Xiaoli(renxl@igsnrr.ac.cn) ; He, Honglin(hehl@igsnrr.ac.cn)
英文摘要	Effective and accurate monitoring of grassland aboveground biomass (AGB) is necessary for improving our understanding of regional carbon cycle and pastoral agricultural management. In this study, we developed a suitable AGB estimation model for the Tibetan alpine grasslands based on the random forest algorithm, using 256 AGB observation data, remote sensing vegetation indices, meteorological data, and topographical data. We estimated the grassland AGB on the Tibetan Plateau during 2000-2014, analyzed its spatiotemporal changes, and further explored the response of AGB to the variation in climatic factors. The results indicated that (1) the RF model performed well in the AGB estimation, which can explain 86% of the variation of the observation data. (2) The grassland AGB decreased from the southeast to the northwest in this region, with an average value of 77.12 gm(-2). (3) In the whole study area, the grassland AGB showed significantly positive correlation with temperature and precipitation. The correlation between grassland AGB and MAP was 0.54 (P < 0.05), much higher than that of MAT (R = 0.38, P < 0.05). (4) The inter-annual variation of AGB on the Tibetan Plateau was significantly and positively correlated with temperature (R-2 = 0.45, P < 0.05). This study demonstrated that RF model can help improve our understanding of the spatiotemporal dynamics of the grassland AGB and the effects of climate variation.
资助项目	National Key Research and development program of China[2015CB954102] ; National Basic Work of Science and Technology[2015FY110700] ; National Natural Science Foundation of China[41571424] ; National Natural Science Foundation of China[31700417] ; National Natural Science Foundation of China[41601478] ; Science and Technology Service Network Initiative of Chinese Academy of Science[KFJ-SW-STS-167]
WOS关键词	LIVESTOCK-CARRYING-CAPACITY ; PRECIPITATION GRADIENT ; CARBON STORAGE ; CLIMATE-CHANGE ; TIME-SERIES ; VEGETATION ; CHINA ; ECOSYSTEMS ; EVAPOTRANSPIRATION ; PRODUCTIVITY
WOS研究方向	Biodiversity & Conservation ; Environmental Sciences & Ecology
语种	英语
出版者	ELSEVIER SCIENCE BV
WOS记录号	WOS:000470964500047
资助机构	National Key Research and development program of China ; National Basic Work of Science and Technology ; National Natural Science Foundation of China ; Science and Technology Service Network Initiative of Chinese Academy of Science
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/59254]
专题	中国科学院地理科学与资源研究所
通讯作者	Ren, Xiaoli; He, Honglin
作者单位	1.Chinese Acad Sci, Key Lab Ecosyst Network Observat & Modeling, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Univ Chinese Acad Sci, Coll Resource & Environm, Beijing 100049, Peoples R China 4.Chinese Acad Sci, Key Lab Digital Earth Sci, Inst Remote Sensing & Digital Earth, Beijing 100094, Peoples R China 5.Tianjin Univ, Inst Surface Earth Syst Sci, Tianjin 300072, Peoples R China
推荐引用方式 GB/T 7714	Zeng, Na,Ren, Xiaoli,He, Honglin,et al. Estimating grassland aboveground biomass on the Tibetan Plateau using a random forest algorithm[J]. ECOLOGICAL INDICATORS,2019,102:479-487.
APA	Zeng, Na.,Ren, Xiaoli.,He, Honglin.,Zhang, Li.,Zhao, Dan.,...&Niu, Zhongen.(2019).Estimating grassland aboveground biomass on the Tibetan Plateau using a random forest algorithm.ECOLOGICAL INDICATORS,102,479-487.
MLA	Zeng, Na,et al."Estimating grassland aboveground biomass on the Tibetan Plateau using a random forest algorithm".ECOLOGICAL INDICATORS 102(2019):479-487.