干旱区生态环境知识资源中心(Arid): Green Vegetation Cover Dynamics in a Heterogeneous Grassland: Spectral Unmixing of Landsat Time Series from 1999 to 2014

Arid

DOI	10.3390/rs12223826
	Green Vegetation Cover Dynamics in a Heterogeneous Grassland: Spectral Unmixing of Landsat Time Series from 1999 to 2014
	He, Yuhong; Yang, Jian; Guo, Xulin
通讯作者	He, YH
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2020
卷号	12 期号:22
英文摘要	The ability to quantify green vegetation across space and over time is useful for studying grassland health and function and improving our understanding of the impact of land use and climate change on grasslands. Directly measuring the fraction of green vegetation cover is labor-intensive and thus only practical on relatively smaller experimental sites. Remote sensing vegetation indices, as a commonly-used method for large-area vegetation mapping, were found to produce inconsistent accuracies when mapping green vegetation in semi-arid grasslands, largely due to mixed pixels including both photosynthetic and non-photosynthetic material. The spectral mixture approach has the potential to map the fraction of green vegetation cover in a heterogeneous landscape, thanks to its ability to decompose a spectral signal from a mixed pixel into a set of fractional abundances. In this study, a time series of fractional green vegetation cover (FGVC) from 1999 to 2014 is estimated using the spectral mixture approach for a semi-arid mixed grassland, which represents a typical threatened, species-rich habitat in Central Canada. The shape of pixel clouds in each of the Landsat images is used to identify three major image endmembers (green vegetation, bare soil/litter, and water/shadow) for automated image spectral unmixing. The FGVC derived through the spectral mixture approach correlates highly with field observations (R-2 = 0.86). Change in the FGVC over the study period was also mapped, and green vegetation in badlands and uplands is found to experience a slight increase, while vegetation in riparian zone shows a decrease. Only a small portion of the study area is undergoing significant changes, which is likely attributable to climate variability, bison reintroduction, and wildfire. The results of this study suggest that the automated spectral unmixing approach is promising, and the time series of medium-resolution images is capable of identifying changes in green vegetation cover in semi-arid grasslands. Further research should investigate driving forces for areas undergoing significant changes.
英文关键词	fractional green vegetation cover spatial and temporal variations spectral unmixing automated image endmember selection semi-arid grasslands
类型	Article
语种	英语
开放获取类型	gold, Green Published
收录类别	SCI-E
WOS记录号	WOS:000594607000001
WOS关键词	REMOTE-SENSING DATA ; CHLOROPHYLL CONTENT ; MIXTURE ANALYSIS ; TEMPORAL DYNAMICS ; MIXED GRASSLAND ; INDEX ; LEAF ; PRAIRIE ; URBAN ; ORTHOGONALITY
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/2XILL650/327786
作者单位	[He, Yuhong; Yang, Jian] Univ Toronto Mississauga, Dept Geog Geomat & Environm, 3359 Mississauga Rd, Mississauga, ON L5L 1C6, Canada; [Guo, Xulin] Univ Saskatchewan, Dept Geog & Planning, Saskatoon, SK S7N 5C8, Canada
推荐引用方式 GB/T 7714	He, Yuhong,Yang, Jian,Guo, Xulin. Green Vegetation Cover Dynamics in a Heterogeneous Grassland: Spectral Unmixing of Landsat Time Series from 1999 to 2014[J],2020,12(22).
APA	He, Yuhong,Yang, Jian,&Guo, Xulin.(2020).Green Vegetation Cover Dynamics in a Heterogeneous Grassland: Spectral Unmixing of Landsat Time Series from 1999 to 2014.REMOTE SENSING,12(22).
MLA	He, Yuhong,et al."Green Vegetation Cover Dynamics in a Heterogeneous Grassland: Spectral Unmixing of Landsat Time Series from 1999 to 2014".REMOTE SENSING 12.22(2020).