干旱区生态环境知识资源中心(Arid): High resolution remote sensing recognition of elm sparse forest via deep-learning-based semantic segmentation

Arid

DOI	10.1016/j.ecolind.2024.112428
	High resolution remote sensing recognition of elm sparse forest via deep-learning-based semantic segmentation
	Liu, Hao; Sun, Bin; Gao, Zhihai; Chen, Zhulin; Zhu, Zhongzheng
通讯作者	Sun, B
来源期刊	ECOLOGICAL INDICATORS
ISSN	1470-160X
EISSN	1872-7034
出版年	2024
卷号	166
英文摘要	Elm (Ulmus pumila L.) sparse forest plays an vital role in maintaining local ecological stability and security in the Otingdag Sandy Land area. Prior studies on elm canopy extraction have predominantly relied on manual parameter configuration, resulting in unsatisfactory levels of generalization. To meet the needs of high-precision and rapid recognition of elm sparse forests in large areas, this study proposed a recognition method for elm sparse forest that orients to high spatial resolution remote sensing imageries, using deep-learning-based semantic segmentation techniques. It can automatically learn features that are conducive to segmenting the canopy of elm trees, and retains good generalization ability on the Gaofen-2 imageries obtained in different regions. First, we constructed a dataset specialized for elm canopy semantic segmentation task, and annotated over 130,000 elm canopies based on Gaofen-2 imageries. In addition, we trained 7 deep-learning semantic segmentation model candidates. Among them, MANet showed the best performance, with its F1-score reaching 81.44%. Lastly, we applied edge detection to the elm canopy coverage area, and automatically extract the elm canopy. The proposed method can provide technical support for the investigation and monitoring of elm sparse forests, while facilitates local desertification prevention efforts in the entire Otingdag Sandy Region.
英文关键词	Otingdag Sandy Land Elm Sparse Forest Gaofen-2 Semantic Segmentation Deep Learning
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001284665000001
WOS关键词	NETWORK
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/2XILL650/403426
推荐引用方式 GB/T 7714	Liu, Hao,Sun, Bin,Gao, Zhihai,et al. High resolution remote sensing recognition of elm sparse forest via deep-learning-based semantic segmentation[J],2024,166.
APA	Liu, Hao,Sun, Bin,Gao, Zhihai,Chen, Zhulin,&Zhu, Zhongzheng.(2024).High resolution remote sensing recognition of elm sparse forest via deep-learning-based semantic segmentation.ECOLOGICAL INDICATORS,166.
MLA	Liu, Hao,et al."High resolution remote sensing recognition of elm sparse forest via deep-learning-based semantic segmentation".ECOLOGICAL INDICATORS 166(2024).