Remote sensing inversion of grassland aboveground biomass based on high accuracy surface modeling

doi:10.1016/j.ecolind.2020.107215

	Remote sensing inversion of grassland aboveground biomass based on high accuracy surface modeling
	Zhou, Wei 1,2,3; Li, Haoran 3; Xie, Lijuan 3; Nie, Xuemin 4; Wang, Zong 1; Du, Zhengping 1; Yue, Tianxiang 1
刊名	ECOLOGICAL INDICATORS
	2021-02-01
卷号	121 页码:10
关键词	Aboveground biomass High accuracy surface modelling Random forest Driving mechanism Three-rivers source region
ISSN号	1470-160X
DOI	10.1016/j.ecolind.2020.107215
通讯作者	Yue, Tianxiang(yue@lreis.ac.cn)
英文摘要	Accurate and effective accounting of grassland aboveground biomass (AGB) is essential for grassland carbon storage accounting and pastoral agriculture sustainability. In this study, we combined AGB field survey data and remote sensing data to build a suitable model to estimate the grassland AGB in the Three-River Source Region (TRSR) of China. Three machine learning models were used to simulate the grassland AGB from 2001 to 2019, including support vector machine (SVM), random forest (RF), and high accuracy surface modeling (HASM). The results show that (1) the HASM achieved better results than the RF and SVM models (R-2 = 0.8459 > 0.72 > 0.5858; RMSE = 29 < 41 < 56), and the HASM results reproduce the spatial distribution characteristics of the biomass well. The subsequent spatiotemporal analysis of the AGB conducted in this study was based on the results of the HASM. (2) The highest AGB was located in the eastern and central-southern parts of the TRSR, and the lowest AGB was distributed in the western region. (3) The overall change in the AGB revealed that the percentage of the area that experienced a significant increase in AGB (21%) was larger than that of the area that experienced a significant decrease (13%), and the stable areas accounted for 66% of the total area. The grassland AGB increased by 1 g/m(2)/yr during 2001-2019. (4) The factors driving the changes in the AGB were analyzed. Overall, the warm and wet climate conditions promoted grass growth in most regions of the TRSR. (5) However, the biomass decreased in some regions with warm and wet conditions. For example, overgrazing and increased populations of grazing led to significant biomass decreases in the towns of Ziketang and Heka in Xinghai County. In this study, the grassland AGB was simulated based on the HASM model, with a high accuracy and a spatial resolution of 500 m. The results of this study provide a scientific basis for grassland resource protection and the highly effective implementation of grassland restoration projects in China.
资助项目	China Postdoctoral Science Foundation[2019M650821] ; Scientific and Technological Research Program of the Chongqing Municipal Education Commission[KJQN201800702] ; National Natural Science Foundation of China[41977337] ; National Natural Science Foundation of China[41501575]
WOS关键词	INNER-MONGOLIA ; ECOSYSTEM CHANGE ; CHINA ; DEGRADATION ; DERIVATION ; DYNAMICS ; IMAGERY ; REGION ; LAND
WOS研究方向	Biodiversity & Conservation ; Environmental Sciences & Ecology
语种	英语
出版者	ELSEVIER
WOS记录号	WOS:000604890000011
资助机构	China Postdoctoral Science Foundation ; Scientific and Technological Research Program of the Chongqing Municipal Education Commission ; National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/136744]
专题	中国科学院地理科学与资源研究所
通讯作者	Yue, Tianxiang
作者单位	1.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 2.Southwest Univ, Sch Geog Sci, Chongqing 400715, Peoples R China 3.Chongqing Jiaotong Univ, Dept Geog & Land & Resources, Chongqing 400074, Peoples R China 4.Qinghai Ecol Environm Monitoring Ctr, Xining 810007, Peoples R China
推荐引用方式 GB/T 7714	Zhou, Wei,Li, Haoran,Xie, Lijuan,et al. Remote sensing inversion of grassland aboveground biomass based on high accuracy surface modeling[J]. ECOLOGICAL INDICATORS,2021,121:10.
APA	Zhou, Wei.,Li, Haoran.,Xie, Lijuan.,Nie, Xuemin.,Wang, Zong.,...&Yue, Tianxiang.(2021).Remote sensing inversion of grassland aboveground biomass based on high accuracy surface modeling.ECOLOGICAL INDICATORS,121,10.
MLA	Zhou, Wei,et al."Remote sensing inversion of grassland aboveground biomass based on high accuracy surface modeling".ECOLOGICAL INDICATORS 121(2021):10.