干旱区生态环境知识资源中心(Arid): Estimating green biomass ratio with remote sensing in arid grasslands

Arid

DOI	10.1016/j.ecolind.2018.11.043
	Estimating green biomass ratio with remote sensing in arid grasslands
	Ren, Hongrui 1; Zhou, Guangsheng 2
通讯作者	Ren, Hongrui ; Zhou, Guangsheng
来源期刊	ECOLOGICAL INDICATORS
ISSN	1470-160X
EISSN	1872-7034
出版年	2019
卷号	98 页码:568-574
英文摘要	It is difficult to estimate green biomass ratio (GBR), the ratio of green aboveground biomass to total aboveground biomass, using common broad-band vegetation indices in arid grasslands due to similar spectral features between bare soil and non-photosynthetic vegetation in near-infrared (NIR) and visible bands. We evaluated the performance of the broad-band RVI (ratio vegetation index), NDVI (normalized difference vegetation index), SAVI (soil-adjusted vegetation index), MSAVI (modified soil-adjusted vegetation index), OSAVI (optimized soil adjusted vegetation index), NDVIgreen (green normalized difference vegetation index), CI (canopy index), and NCI (normalized canopy index) for GBR estimation in the desert steppe of Inner Mongolia, China. We also explored best narrow-band hyperspectral vegetation indices for GBR estimation using hyperspectral remotely sensed data and GBR measurements during 2009 and 2010 growing seasons in the desert steppe. Broad-band vegetation indices were not suitable for GBR estimation. The best narrow-band vegetation indices used reflectance at 2069 and 2042 nm; particular 1.5 x (R-2069 - R-2042)/(R-2069 + R-2042 + 0.5). The index could partially overcome the influence of bare soil cover. It explained 68% of the variance of GBR and dramatically improved GBR estimation accuracy over common broad-band indices. More importantly, the accuracy was not affected by varying bare soil cover. Nevertheless, caution is required for the index application within varying growing seasons. The development of this index is an important resource for future spectral sensors that will permit GBR monitoring at regional scales in arid grasslands. Our results show that remote imagery can monitor GBR in the desert steppe and potentially in many arid grasslands.
英文关键词	Green biomass ratio Arid grasslands Remote sensing Hyperspectral vegetation indices Medium-infrared bands
类型	Article
语种	英语
国家	Peoples R China
收录类别	SCI-E
WOS记录号	WOS:000464891100056
WOS关键词	DESERT STEPPE ; VEGETATION ; MODEL ; REFLECTANCE ; SIMULATION ; INDEX ; RED
WOS类目	Biodiversity Conservation ; Environmental Sciences
WOS研究方向	Biodiversity & Conservation ; Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/215195
作者单位	1.Taiyuan Univ Technol, Coll Min Engn, Dept Geomat, 79 West Yingze St, Taiyuan 030024, Shanxi, Peoples R China; 2.Chinese Acad Meteorol Sci, 46 Zhongguancun South St, Beijing 100081, Peoples R China
推荐引用方式 GB/T 7714	Ren, Hongrui,Zhou, Guangsheng. Estimating green biomass ratio with remote sensing in arid grasslands[J],2019,98:568-574.
APA	Ren, Hongrui,&Zhou, Guangsheng.(2019).Estimating green biomass ratio with remote sensing in arid grasslands.ECOLOGICAL INDICATORS,98,568-574.
MLA	Ren, Hongrui,et al."Estimating green biomass ratio with remote sensing in arid grasslands".ECOLOGICAL INDICATORS 98(2019):568-574.