干旱区生态环境知识资源中心(Arid): Estimating fractional green vegetation cover of Mongolian grasslands using digital camera images and MODIS satellite vegetation indices

Arid

DOI	10.1080/15481603.2019.1662166
	Estimating fractional green vegetation cover of Mongolian grasslands using digital camera images and MODIS satellite vegetation indices
	Kim, Jaebeom 1; Kang, Sinkyu 1; Seo, Bumsuk 2; Narantsetseg, Amratuvshin 3; Han, Youngji 1
通讯作者	Kang, Sinkyu
来源期刊	GISCIENCE & REMOTE SENSING
ISSN	1548-1603
EISSN	1943-7226
出版年	2020
卷号	57 期号:1 页码:49-59
英文摘要	Fractional green vegetation cover (FVC) is a useful indicator for monitoring grassland status. Satellite imagery with coarse spatial but high temporal resolutions has been preferred to monitor seasonal and inter-annual FVC dynamics in wide geographic area such as Mongolian steppe. However, the coarse spatial resolution can cause a certain uncertainty in the satellite-based FVC estimation, which calls attention to develop a robust statistical test for the relationship between field FVC and satellite-derived vegetation indices. In the arid and semi-arid Mongolian steppe, nadir pointing digital camera images (DCI) were collected and used to produce a FVC dataset to support the evaluation of satellite-based FVC retrievals. An optimal DCI processing method was determined with respect to three color spaces (RGB, HIS, Lab*) and six green pixel classification algorithms, from which a country-wide dataset of DCI-FVC was produced and used for evaluating the accuracy of satellite-based FVC estimates from MODIS vegetation indices. We applied three empirical and three semi-empirical MODIS-FVC retrieval models. DCI data were collected from 96 sites across the Mongolian steppe from 2012 to 2014. The histogram algorithm using the hue (H) value of the HIS color space was the optimal DCI method (r(2) = 0.94, percent root-mean-square-error (RMSE) = 7.1%). For MODIS-FVC retrievals, semi-empirical Baret model was the best-performing model with the highest r(2) (0.69) and the lowest RMSE (49.7%), while the lowest MB (+1.1%) was found for the regression model with normalized difference vegetation index (NDVI). The high RMSE (>50% or so) is an issue requiring further enhancement of satellite-based FVC retrievals accounting for key plant and soil parameters relevant to the Mongolian steppe and for scale mismatch between sampling and MODIS data.
英文关键词	Digital camera image fractional green vegetation cover Mongolian steppe satellite vegetation indices
类型	Article
语种	英语
国家	South Korea ; Germany ; Mongolia
收录类别	SCI-E
WOS记录号	WOS:000485036800001
WOS关键词	AREA ; DESERTIFICATION ; DISASTER ; DROUGHT ; MODEL ; NDVI
WOS类目	Geography, Physical ; Remote Sensing
WOS研究方向	Physical Geography ; Remote Sensing
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/216058
作者单位	1.Kangwon Natl Univ, Dept Environm Sci, Chunchon, South Korea; 2.KIT, Inst Meteorol & Climate Res Atmospher Environm Re, Garmisch Partenkirchen, Germany; 3.Mongolian Acad Sci, Inst Gen & Expt Biol, Ulaanbaatar, Mongolia
推荐引用方式 GB/T 7714	Kim, Jaebeom,Kang, Sinkyu,Seo, Bumsuk,et al. Estimating fractional green vegetation cover of Mongolian grasslands using digital camera images and MODIS satellite vegetation indices[J],2020,57(1):49-59.
APA	Kim, Jaebeom,Kang, Sinkyu,Seo, Bumsuk,Narantsetseg, Amratuvshin,&Han, Youngji.(2020).Estimating fractional green vegetation cover of Mongolian grasslands using digital camera images and MODIS satellite vegetation indices.GISCIENCE & REMOTE SENSING,57(1),49-59.
MLA	Kim, Jaebeom,et al."Estimating fractional green vegetation cover of Mongolian grasslands using digital camera images and MODIS satellite vegetation indices".GISCIENCE & REMOTE SENSING 57.1(2020):49-59.