干旱区生态环境知识资源中心(Arid): New research methods for vegetation information extraction based on visible light remote sensing images from an unmanned aerial vehicle (UAV)

Arid

DOI	10.1016/j.jag.2019.01.001
	New research methods for vegetation information extraction based on visible light remote sensing images from an unmanned aerial vehicle (UAV)
	Zhang, Xianlong 1,2,3; Zhang, Fei1,2,3 ; Qi, Yaxiao 1,2,3; Deng, Laifei 1,2,3; Wang, Xiaolong 4; Yang, Shengtian 5
通讯作者	Zhang, Fei
来源期刊	INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
ISSN	1569-8432
EISSN	1872-826X
出版年	2019
卷号	78 页码:215-226
英文摘要	Currently, many remote sensing images of the vegetation index being used have disadvantages, because of high cost, long cycles, and low resolution. Thus, it is difficult to extract and analyse vegetation information in the field. A vegetation index based on visible light images from an unmanned aerial vehicle (UAV) has the advantages of fast image acquisition and high ground resolution, which is superior to traditional remote sensing. However, the vegetation coverage in arid and semi-arid areas is low, and the soil background has a great impact on the common visible vegetation index. The real-time extraction and analysis of the index vegetation information can easily result in big errors. Therefore, according to the construction principle of the green-red vegetation index (GRVI) and modified green-red vegetation index (MGRVI), a new green-red vegetation index (NGRVI) is proposed in this study. First, the newly constructed index and several published indices are used to extract visible light images and generate greyscale images for each of the visible light vegetation indices. Then, the threshold of vegetation and non-vegetation pixel classification is established according to the method of iterative threshold, and the optimal threshold is used to extract the vegetation information from the greyscale images of each of the visible light vegetation indices. Finally, the accuracy difference in vegetation information extraction between the newly constructed and several published indices is compared. The results show that the precision of vegetation information extraction by NGRVI is higher than that of other visible light band vegetation indices; the kappa coefficient is 0.82, and the classification accuracy reaches near-complete consistency. To verify the accuracy of the NGRVI, one image from the same period was selected, and the vegetation information was extracted using the same method. The NGRVI based on UAV visible light images can accurately extract the vegetation information in arid and semi-arid areas, and the extraction accuracy can reach more than 90%. To summarize, NGRVI can accurately and effectively reflect the vegetation information in arid and semi-arid areas and become an important technical means for retrieving biological and physical parameters using visible light images.
英文关键词	Iterative threshold method Unmanned aerial vehicle (UAV) Visible light images Vegetation information extraction Construction of NGRVI
类型	Article
语种	英语
国家	Peoples R China
收录类别	SCI-E
WOS记录号	WOS:000463131700019
WOS关键词	PER-PIXEL ; CLASSIFICATION ; IDENTIFICATION ; INDEXES ; WATER
WOS类目	Remote Sensing
WOS研究方向	Remote Sensing
来源机构	北京师范大学 ; 新疆大学
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/216340
作者单位	1.Xinjiang Univ, Coll Resources & Environm Sci, Key Lab Smart City & Environm, Higher Educ Inst, Urumqi 830046, Peoples R China; 2.Xinjiang Univ, Key Lab Oasis Ecol, Urumqi 830046, Peoples R China; 3.Natl Adm Surveying Mapping & Geoinformat, Engn Res Ctr Cent Asia Geoinformat Dev & Utilizat, Urumqi 830002, Peoples R China; 4.Beijing Normal Univ, Sch Geog, State Key Lab Remote Sensing Sci, Beijing Key Lab Remote Sensing Environm & Digital, Beijing 100875, Peoples R China; 5.CAAS, Inst Agr Resources & Reg Planning, Beijing 100081, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Xianlong,Zhang, Fei,Qi, Yaxiao,et al. New research methods for vegetation information extraction based on visible light remote sensing images from an unmanned aerial vehicle (UAV)[J]. 北京师范大学, 新疆大学,2019,78:215-226.
APA	Zhang, Xianlong,Zhang, Fei,Qi, Yaxiao,Deng, Laifei,Wang, Xiaolong,&Yang, Shengtian.(2019).New research methods for vegetation information extraction based on visible light remote sensing images from an unmanned aerial vehicle (UAV).INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,78,215-226.
MLA	Zhang, Xianlong,et al."New research methods for vegetation information extraction based on visible light remote sensing images from an unmanned aerial vehicle (UAV)".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 78(2019):215-226.