干旱区生态环境知识资源中心(Arid): Estimating the fractional cover of photosynthetic vegetation, non-photosynthetic vegetation and bare soil from MODIS data: Assessing the applicability of the NDVI-DFI model in the typical Xilingol grasslands

Arid

DOI	10.1016/j.jag.2018.11.006
	Estimating the fractional cover of photosynthetic vegetation, non-photosynthetic vegetation and bare soil from MODIS data: Assessing the applicability of the NDVI-DFI model in the typical Xilingol grasslands
	Wang, Guangzhen 1; Wang, Jingpu 1; Zou, Xueyong 2; Chai, Guoqi 1; Wu, Mengquan 1; Wang, Zhoulong 1
通讯作者	Wang, Jingpu
来源期刊	INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
ISSN	1569-8432
EISSN	1872-826X
出版年	2019
卷号	76 页码:154-166
英文摘要	Quantitative estimations of the fractional cover of photosynthetic vegetation (f(PV)), non-photosynthetic vegetation (f(NPV)) and bare soil (f(BS)) are critical for soil wind erosion, desertification, grassland grazing, grassland fire, and grassland carbon storage studies. At present, regional and large-scale f(PV), f(NPV) and f(BS) estimations have been carried out in many areas. However, few studies have used moderate resolution imaging spectroradiometer (MODIS) data to perform large-scale, long-term f(PV), f(NPV) and f(BS) estimations in the Xilingol grassland of China. The objective of this study was to quantitatively estimate the time series of f(PV), f(NPV) and f(BS) in the typical grassland region of Xilingol from MODIS image data. Field measurement spectral and coverage data from May and September 2017 were combined with the 8-day composite product (MOD09A1) acquired during 2017. We established an empirical linear model of different non-photosynthetic vegetation indices (NPVIs) and f(NPV) based on the sample scale. The linear correlation between the dead fuel index (DFI) and f(NPV) was best (R-2 = 0.60, RMSE = 0.15). A normalized difference vegetation index (NDVI)-DFI model based on MODIS data was proposed to accurately estimate the f(PV), f(NPV) and f(BS) (estimation accuracies of 44%, 71%, and 74%, respectively) in the typical grasslands of Xilingol in China. The f(PV), f(NPV) and f(BS) values for the typical grassland time series estimated by the NDVI-DFI model were consistent with the phenological characteristics of the grassland vegetation. The results show that the application of the NDVI-DFI model to the Xilingol grassland is reasonable and appropriate, and it is of great significance to the monitoring of soil wind erosion and fires in grasslands.
英文关键词	Non-photosynthetic vegetation Fractional cover Bare soil Xilingol grassland MODIS DFI
类型	Article
语种	英语
国家	Peoples R China
收录类别	SCI-E
WOS记录号	WOS:000457660800013
WOS关键词	CROP RESIDUE COVER ; AUSTRALIAN TROPICAL SAVANNA ; INDEX ; DECOMPOSITION ; DYNAMICS ; STEPPE ; REFLECTANCE ; HYPERION ; BIOMASS ; IMPACT
WOS类目	Remote Sensing
WOS研究方向	Remote Sensing
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/2XILL650/216339
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 100875, Peoples R China
推荐引用方式 GB/T 7714	Wang, Guangzhen,Wang, Jingpu,Zou, Xueyong,et al. Estimating the fractional cover of photosynthetic vegetation, non-photosynthetic vegetation and bare soil from MODIS data: Assessing the applicability of the NDVI-DFI model in the typical Xilingol grasslands[J]. 北京师范大学,2019,76:154-166.
APA	Wang, Guangzhen,Wang, Jingpu,Zou, Xueyong,Chai, Guoqi,Wu, Mengquan,&Wang, Zhoulong.(2019).Estimating the fractional cover of photosynthetic vegetation, non-photosynthetic vegetation and bare soil from MODIS data: Assessing the applicability of the NDVI-DFI model in the typical Xilingol grasslands.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,76,154-166.
MLA	Wang, Guangzhen,et al."Estimating the fractional cover of photosynthetic vegetation, non-photosynthetic vegetation and bare soil from MODIS data: Assessing the applicability of the NDVI-DFI model in the typical Xilingol grasslands".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 76(2019):154-166.