干旱区生态环境知识资源中心(Arid): Grassland Biomass Inversion Based on a Random Forest Algorithm and Drought Risk Assessment

Arid

DOI	10.3390/rs14225745
	Grassland Biomass Inversion Based on a Random Forest Algorithm and Drought Risk Assessment
	Bu, Lingxin; Lai, Quan; Qing, Song; Bao, Yuhai; Liu, Xinyi; Na, Qin; Li, Yuan
通讯作者	Lai, Q
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2022
卷号	14 期号:22
英文摘要	Xilin Gol is a typical kind of grassland in arid and semi-arid regions. Under climate warming, the droughts faced by various grassland types tend to expand in scope and intensity, and increase in frequency. Therefore, the quantitative analysis of drought risk in different grassland types becomes particularly important. Based on multi-source data, a random forest regression algorithm was used to construct a grassland biomass estimation model, which was then used to analyze the spatiotemporal variation characteristics of grassland biomass. A quantitative assessment of drought risk (DR) in different grassland types was applied based on the theory of risk formation, and a structural equation model (SEM) was used to analyze the drivers of drought risk in different grassland types. The results show that among the eight selected variables that affect grassland biomass, the model had the highest accuracy (R = 0.90) when the normalized difference vegetation index (NDVI), precipitation (Prcp), soil moisture (SM) and longitude (Lon) were combined as input variables. The grassland biomass showed a spatial distribution that was high in the east and low in the west, gradually decreasing from northeast to southwest. Among the grasslands, desert grassland (DRS) had the highest drought risk (DR = 0.30), while meadow grassland (MEG) had the lowest risk (DR = 0.02). The analysis of the drivers of drought risk in grassland biomass shows that meteorological elements mainly drive typical grasslands (TYG) and other grasslands (OTH). SM greatly impacted MEG, and ET had a relatively high contribution to DRS. This study provides a basis for managing different grassland types in large areas and developing corresponding drought adaptation programs.
英文关键词	biomass inversion prairie drought risk climate variability human activities
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000887709500001
WOS关键词	CLIMATE-CHANGE ; LOESS PLATEAU ; TIME-SCALES ; VEGETATION ; CHINA ; WATER ; NDVI ; EVAPOTRANSPIRATION ; VULNERABILITY ; PRODUCTIVITY
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/394232
推荐引用方式 GB/T 7714	Bu, Lingxin,Lai, Quan,Qing, Song,et al. Grassland Biomass Inversion Based on a Random Forest Algorithm and Drought Risk Assessment[J],2022,14(22).
APA	Bu, Lingxin.,Lai, Quan.,Qing, Song.,Bao, Yuhai.,Liu, Xinyi.,...&Li, Yuan.(2022).Grassland Biomass Inversion Based on a Random Forest Algorithm and Drought Risk Assessment.REMOTE SENSING,14(22).
MLA	Bu, Lingxin,et al."Grassland Biomass Inversion Based on a Random Forest Algorithm and Drought Risk Assessment".REMOTE SENSING 14.22(2022).