干旱区生态环境知识资源中心(Arid): Estimating fractional cover of non-photosynthetic vegetation in a typical grassland area of northern China based on Moderate Resolution Imaging Spectroradiometer (MODIS) image data

Arid

DOI	10.1080/01431161.2019.1620971
	Estimating fractional cover of non-photosynthetic vegetation in a typical grassland area of northern China based on Moderate Resolution Imaging Spectroradiometer (MODIS) image data
	Chai, Guoqi 1; Wang, Jingpu 1; Wang, Guangzhen 1; Kang, Liqiang 2; Wu, Mengquan 1; Wang, Zhoulong 1
通讯作者	Wang, Jingpu
来源期刊	INTERNATIONAL JOURNAL OF REMOTE SENSING
ISSN	0143-1161
EISSN	1366-5901
出版年	2019
卷号	40 期号:23 页码:8793-8810
英文摘要	Rapid accurate estimation of the fractional cover of non-photosynthetic vegetation (f(NPV)) is essential for monitoring desertification, managing grassland resources, assessing soil erosion and grassland fire risk, and preserving the grassland ecological environment. However, there have been very few studies using multispectral remote sensing images (e.g. Moderate Resolution Imaging Spectroradiometer (MODIS) images in this study) to estimate f(NPV) in typical grassland areas in northern China. In this study, using field spectra obtained from ground measurements in May and October 2017 and corresponding f(NPV) data, we calculated eight non-photosynthetic vegetation indices (NPVIs) from the simulated MODIS bands. We then determined the NPVIs that were suitable for the estimation of f(NPV). Based on the determined NPVIs, we established a remote sensing estimation model for f(NPV) in typical grassland areas using MODIS image data. The spatial distribution of f(NPV) in the studied area was also investigated. The results indicated that the determined NPVIs, including the dead fuel index (DFI), shortwave-infrared ratio (SWIR32), normalized difference tillage index (NDTI), modified soil-adjusted crop residue index (MSACRI), and soil tillage index (STI), used bands 6 and 7 in the shortwave-infrared region of the MODIS data; the DFI had the best performance, with a coefficient of determination (R-2) of 0.68 and root mean square error of leave-one-out cross-validation (RMSECV) of 0.1390. The models based on MODIS image data for the estimation of f(NPV) using NPVIs had relatively good regression relations, and we determined that the DFI linear regression model was the best remote sensing model for monitoring f(NPV) in typical grassland areas, with an estimation accuracy exceeding 73.00%. Additionally, our results indicated that the distribution of non-photosynthetic vegetation exhibited substantial spatial heterogeneity and that f(NPV) gradually decreased from the north-eastern to south-western portions of the study area.
类型	Article
语种	英语
国家	Peoples R China
收录类别	SCI-E
WOS记录号	WOS:000470581000001
WOS关键词	CROP RESIDUE COVER ; PHOTOSYNTHETIC VEGETATION ; BIOMASS ESTIMATION ; BARE SOIL ; DECOMPOSITION ; STEPPE ; INDEX ; FUEL ; MASS
WOS类目	Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Remote Sensing ; Imaging Science & Photographic Technology
来源机构	北京师范大学
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/216506
作者单位	1.Ludong Univ, Coll Resource & Environm Engn, Yantai 264025, Peoples R China; 2.Beijing Normal Univ, State Key Lab Earth Surface Proc & Resource Ecol, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Chai, Guoqi,Wang, Jingpu,Wang, Guangzhen,et al. Estimating fractional cover of non-photosynthetic vegetation in a typical grassland area of northern China based on Moderate Resolution Imaging Spectroradiometer (MODIS) image data[J]. 北京师范大学,2019,40(23):8793-8810.
APA	Chai, Guoqi,Wang, Jingpu,Wang, Guangzhen,Kang, Liqiang,Wu, Mengquan,&Wang, Zhoulong.(2019).Estimating fractional cover of non-photosynthetic vegetation in a typical grassland area of northern China based on Moderate Resolution Imaging Spectroradiometer (MODIS) image data.INTERNATIONAL JOURNAL OF REMOTE SENSING,40(23),8793-8810.
MLA	Chai, Guoqi,et al."Estimating fractional cover of non-photosynthetic vegetation in a typical grassland area of northern China based on Moderate Resolution Imaging Spectroradiometer (MODIS) image data".INTERNATIONAL JOURNAL OF REMOTE SENSING 40.23(2019):8793-8810.