干旱区生态环境知识资源中心(Arid): Desert oasis vegetation information extraction by PLANET and unmanned aerial vehicle image fusion

Arid

DOI	10.1016/j.ecolind.2024.112516
	Desert oasis vegetation information extraction by PLANET and unmanned aerial vehicle image fusion
	Guo, Yuchuan; Wang, Ning; Wei, Xuan; Zhou, Mingtong; Wang, Huijing; Bai, Yunbao
通讯作者	Guo, YC
来源期刊	ECOLOGICAL INDICATORS
ISSN	1470-160X
EISSN	1872-7034
出版年	2024
卷号	166
英文摘要	Sparse vegetation is a key factor in maintaining the health and sustainability of oasis ecosystems under extreme drought conditions. Combining the advantages of both satellites and drones, using image processing and machine learning technology, it can efficiently and accurately extract information on the vegetation of desert oases, providing a scientific basis for ecological monitoring and management. Therefore, in this study, the Dariyabui natural oasis in the hinterland of the Taklamakan Desert, China, was used as the study area to fuse PLANET and UAV images to screen 19 feature factors from different data sources. The machine learning binary classification model was evaluated based on the overall accuracy (OA, %), kappa, balanced accuracy (BA, %), F1_score (F1, %), and Area Under Curve (AUC) values. The results were validated using aerial imaging data from different months. The results show that (1) the primary feature factors for extracting sparse ground vegetation information in a desert oasis include the soil-adjusted vegetation index (SAVI); modified soil-adjusted vegetation index (MSAVI); optimized soil-adjusted vegetation index (OSAVI); and the mean, entropy, contrast, homogeneity, and digital surface models (DSM). (2) The evaluation indices of the fused images in the vegetation information extraction were better than those of the original satellite images; the difference in the extraction effect with the original UAV images was not significant. (3) The random forest (RF) algorithm achieved the best classification accuracy in extracting sparse vegetation information (Kappa = 0.87, OA = 94.12 %, BA = 93.53 %, F1 = 92.13 %, and AUC = 0.967).
英文关键词	Oasis sparse vegetation PLANET satellite remote sensing UAV remote sensing Image fusion Machine learning dichotomous classification
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001300168900001
WOS关键词	OBJECT-BASED CLASSIFICATION ; RANDOM FOREST ; LAND-COVER ; INDEXES ; ALGORITHMS ; PERFORMANCE
WOS类目	Biodiversity Conservation ; Environmental Sciences
WOS研究方向	Biodiversity & Conservation ; Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/403421
推荐引用方式 GB/T 7714	Guo, Yuchuan,Wang, Ning,Wei, Xuan,et al. Desert oasis vegetation information extraction by PLANET and unmanned aerial vehicle image fusion[J],2024,166.
APA	Guo, Yuchuan,Wang, Ning,Wei, Xuan,Zhou, Mingtong,Wang, Huijing,&Bai, Yunbao.(2024).Desert oasis vegetation information extraction by PLANET and unmanned aerial vehicle image fusion.ECOLOGICAL INDICATORS,166.
MLA	Guo, Yuchuan,et al."Desert oasis vegetation information extraction by PLANET and unmanned aerial vehicle image fusion".ECOLOGICAL INDICATORS 166(2024).