干旱区生态环境知识资源中心(Arid): Biomass and vegetation coverage survey in the Mu Us sandy land - based on unmanned aerial vehicle RGB images

Arid

DOI	10.1016/j.jag.2020.102239
	Biomass and vegetation coverage survey in the Mu Us sandy land - based on unmanned aerial vehicle RGB images
	Guo Zi-chen; Wang Tao; Liu Shu-lin; Kang Wen-ping; Chen Xiang; Feng Kun; Zhang Xue-qin; Zhi Ying
通讯作者	Liu, SL
来源期刊	INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
ISSN	1569-8432
EISSN	1872-826X
出版年	2021
卷号	94
英文摘要	Accurate detection of vegetation cover and biomass of shrub communities in sandy area is beneficial for evaluating ecosystem, improving remote sensing models, and assessing the accuracy of remote sensing. Unmanned aerial vehicles (UAVs) have replaced traditional measurement methods in biomass and fraction of vegetation coverage (FVC) detection owing to the high spatial resolution of their imagery, their high positioning accuracy, and their ease of use. The existing methods of detecting biomass via UAVs, however, are not suitable for surface fluctuations, dwarf shrubs, and herbs. Futhermore, the method of calculating FVC using UAV RGB images has not yet been tested in sandy areas. To accurately extract FVC data, aboveground biomass (ABS) and net aboveground biomass (NABS) of shrub communities in the desert regions, UAV RGB images of 87 sample plots in the Mu Us sandy land were collected and used to obtain the FVC and biomass information via the object-based classification method, single shrub canopy biomass model and vegetation index-based method. The results are as follows: (1) the method of calculating ABS and NABS based on shrub canopy width extraction can be used in desert shrub communities, and results show that the ABS and NABS of vegetation communities increases from 15 to 800 g/m(2) and 10-250 g/m(2), respectively, in the Mu Us sandy land; and (2) the lowest value of ABS (NABS) appeared in the mobile sandy dunes and the highest value appeared in the semi-fixed sandy dunes; (3) under fixed thresholds conditions, the FVC can be extracted accurately using the excess green method, visible atmospherically resistant index, vegetative index, green red vegetation index and red green blue vegetation index (RGBVI); and (4) the correlation between the FVC calculated by the five RGB vegetation indexes and NABS in this study is greater than that between FVC and ABS (e.g. R-NABS(2) (- RGBVI) = 0.734, R-ABS(2) (- RGBVI) = 0.666), and the FVC calculated by RGBVI can be used to estimate NABS in the Mu Us sandy land. This study will provide new insights for field investigations of the ABS, NABS, and FVC in sandy areas.
英文关键词	UAV Vegetation coverage Aboveground biomass Net aboveground biomass Shrub Mu Us sandy land
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000591457600001
WOS关键词	CROP SURFACE MODELS ; ABOVEGROUND BIOMASS ; UAV ; CLASSIFICATION ; INDEXES ; DESERTIFICATION ; PLATEAU ; SHRUB ; LIDAR ; SOIL
WOS类目	Remote Sensing
WOS研究方向	Remote Sensing
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/328009
作者单位	[Guo Zi-chen; Wang Tao; Liu Shu-lin; Kang Wen-ping; Chen Xiang; Feng Kun; Zhang Xue-qin; Zhi Ying] Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources NIEER, Key Lab Deserts & Desertificat, Donggang West Rd 320, Lanzhou 730000, Gansu, Peoples R China; [Guo Zi-chen; Chen Xiang; Feng Kun; Zhang Xue-qin; Zhi Ying] Univ Chinese Acad Sci UCAS, 19A Yuquan Rd, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Guo Zi-chen,Wang Tao,Liu Shu-lin,et al. Biomass and vegetation coverage survey in the Mu Us sandy land - based on unmanned aerial vehicle RGB images[J],2021,94.
APA	Guo Zi-chen.,Wang Tao.,Liu Shu-lin.,Kang Wen-ping.,Chen Xiang.,...&Zhi Ying.(2021).Biomass and vegetation coverage survey in the Mu Us sandy land - based on unmanned aerial vehicle RGB images.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,94.
MLA	Guo Zi-chen,et al."Biomass and vegetation coverage survey in the Mu Us sandy land - based on unmanned aerial vehicle RGB images".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 94(2021).