干旱区生态环境知识资源中心(Arid): The Potential of Multispectral Vegetation Indices Feature Space for Quantitatively Estimating the Photosynthetic, Non-Photosynthetic Vegetation and Bare Soil Fractions in Northern China

Arid

DOI	10.14358/PERS.85.1.65
	The Potential of Multispectral Vegetation Indices Feature Space for Quantitatively Estimating the Photosynthetic, Non-Photosynthetic Vegetation and Bare Soil Fractions in Northern China
	Zheng, Guoxiong 1,2,3; Bao, Anming1 ; Li, Xiaosong 3; Jiang, Liangliang 1; Chang, Cun 1; Chen, Tao ; Gao, Zhihai 4
通讯作者	Zheng, Guoxiong
来源期刊	PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING
ISSN	0099-1112
EISSN	2374-8079
出版年	2019
卷号	85 期号:1 页码:65-76
英文摘要	Non-photosynthetic vegetation (NPV) is widely distributed in the arid and semi-arid area, especially in the sandy areas. The hyperspectral-based cellulose absorption index (CAI) is an accepted method for estimating the cover fractions of NPV. However, the spaceborne hyperspectral data currently available to us are very limited. In this study, we tried to identify one or more combinations based on the multispectral vegetation indices feature space model to quantitatively estimate the PV, NPV and bare soil fractions of the Otindag Sandy Land in northern China. Three frequently-used green vegetation indices, NDVI, EVI and OSAVI, and nine multispectral-based indices sensitive to NPV were used to examine the spatial patterns based on the field measured endmember spectra and non-growing and growing season Landsat-8 OLI image reflectance spectra. The capabilities of these different combinations were tested in this study area using mosaicked Landsat-8 OLI imagery. The results show that the feature space of different combinations based on the field measured spectra and image reflectance spectra has good consistency. The separability of feature space determines the availability of this model. The normalized difference senescent vegetation index (NDSVI) and brightness index (BI) were found to have greater potential to combine with the three selected green vegetation indices for simultaneous estimation of the fractional cover of PV, NPV, and bare soil in the Otindag Sandy Land because of their clear and separable feature space. We obtained the best and medium-precision estimates for NDVI-NDSVI (f(PV): RMSE=0.26; f(NPV): RMSE=0.17) and OSAVI-BI (f(PV): RMSE=0.27; f(NPV): RMSE=0.25) for 104 field observations.
类型	Article
语种	英语
国家	Peoples R China
收录类别	SCI-E
WOS记录号	WOS:000456707600008
WOS关键词	AUSTRALIAN TROPICAL SAVANNA ; CROP RESIDUE COVER ; OTINDAG SANDY LAND ; EO-1 HYPERION ; DYNAMICS ; FIELD
WOS类目	Geography, Physical ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Physical Geography ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
来源机构	中国科学院新疆生态与地理研究所
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/217946
作者单位	1.Chinese Acad Sci, Xinjiang Inst Ecol & Geog, State Key Lab Desert & Oasis Ecol, Urumqi 830011, Peoples R China; 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China; 3.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China; 4.Chinese Acad Forestry, Inst Forest Resources Informat Tech, Beijing 100091, Peoples R China
推荐引用方式 GB/T 7714	Zheng, Guoxiong,Bao, Anming,Li, Xiaosong,et al. The Potential of Multispectral Vegetation Indices Feature Space for Quantitatively Estimating the Photosynthetic, Non-Photosynthetic Vegetation and Bare Soil Fractions in Northern China[J]. 中国科学院新疆生态与地理研究所,2019,85(1):65-76.
APA	Zheng, Guoxiong.,Bao, Anming.,Li, Xiaosong.,Jiang, Liangliang.,Chang, Cun.,...&Gao, Zhihai.(2019).The Potential of Multispectral Vegetation Indices Feature Space for Quantitatively Estimating the Photosynthetic, Non-Photosynthetic Vegetation and Bare Soil Fractions in Northern China.PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING,85(1),65-76.
MLA	Zheng, Guoxiong,et al."The Potential of Multispectral Vegetation Indices Feature Space for Quantitatively Estimating the Photosynthetic, Non-Photosynthetic Vegetation and Bare Soil Fractions in Northern China".PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING 85.1(2019):65-76.