气候变化领域集成服务门户(CCPortal): Automatedly identify dryland threatened species at large scale by using deep learning

CCPortal

DOI	10.1016/j.scitotenv.2024.170375
	Automatedly identify dryland threatened species at large scale by using deep learning
	Wang, Haolin; Liu, Qi; Gui, Dongwei; Liu, Yunfei; Feng, Xinlong; Qu, Jia; Zhao, Jianping; Wei, Guanghui
发表日期	2024
ISSN	0048-9697
EISSN	1879-1026
起始页码	917
卷号	917
英文摘要	Dryland biodiversity is decreasing at an alarming rate. Advanced intelligent tools are urgently needed to rapidly, automatedly, and precisely detect dryland threatened species on a large scale for biological conservation. Here, we explored the performance of three deep convolutional neural networks (Deeplabv3+, Unet, and Pspnet models) on the intelligent recognition of rare species based on high-resolution (0.3 m) satellite images taken by an unmanned aerial vehicle (UAV). We focused on a threatened species, Populus euphratica, in the Tarim River Basin (China), where there has been a severe population decline in the 1970s and restoration has been carried out since 2000. The testing results showed that Unet outperforms Deeplabv3+ and Pspnet when the training samples are lower, while Deeplabv3+ performs best as the dataset increases. Overall, when training samples are 80, Deeplabv3+ had the best overall performance for Populus euphratica identification, with mean pixel accuracy (MPA) between 87.31 % and 90.2 %, which, on average is 3.74 % and 11.29 % higher than Unet and Pspnet, respectively. Deeplabv3+ can accurately detect the boundaries of Populus euphratica even in areas of dense vegetation, with lower identification uncertainty for each pixel than other models. This study developed a UAV imagery -based identification framework using deep learning with high resolution in large-scale regions. This approach can accurately capture the variation in dryland threatened species, especially those in inaccessible areas, thereby fostering rapid and efficient conservation actions.
英文关键词	Biological conservation; Arid region; Species identification; Remote sensing; Artificial intelligence
语种	英语
WOS研究方向	Environmental Sciences & Ecology
WOS类目	Environmental Sciences
WOS记录号	WOS:001177059900001
来源期刊	SCIENCE OF THE TOTAL ENVIRONMENT
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/297887
作者单位	Chinese Academy of Sciences; Xinjiang Institute of Ecology & Geography, CAS; Xinjiang University; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS
推荐引用方式 GB/T 7714	Wang, Haolin,Liu, Qi,Gui, Dongwei,et al. Automatedly identify dryland threatened species at large scale by using deep learning[J],2024,917.
APA	Wang, Haolin.,Liu, Qi.,Gui, Dongwei.,Liu, Yunfei.,Feng, Xinlong.,...&Wei, Guanghui.(2024).Automatedly identify dryland threatened species at large scale by using deep learning.SCIENCE OF THE TOTAL ENVIRONMENT,917.
MLA	Wang, Haolin,et al."Automatedly identify dryland threatened species at large scale by using deep learning".SCIENCE OF THE TOTAL ENVIRONMENT 917(2024).