干旱区生态环境知识资源中心(Arid): Vegetation coverage precisely extracting and driving factors analysis in drylands

Arid

DOI	10.1016/j.ecoinf.2023.102409
	Vegetation coverage precisely extracting and driving factors analysis in drylands
	Wang, Haolin; Gui, Dongwei; Liu, Qi; Feng, Xinlong; Qu, Jia; Zhao, Jianping; Wang, Guangyan; Wei, Guanghui
通讯作者	Liu, Q
来源期刊	ECOLOGICAL INFORMATICS
ISSN	1574-9541
EISSN	1878-0512
出版年	2024
卷号	79
英文摘要	Fractional Vegetation Coverage (FVC) is an essential indicator that captures variations in vegetation and documents the impacts of climate change and human activity for environmental assessment. However, conventional methods encounter challenges in accurately extracting fine-scale FVC in drylands due to the vegetation distribution being very heterogeneous in space with patches and inter-patches. Using the lower Tarim River Basin as a typical study case, we investigated three deep convolutional neural networks-Unet, Pspnet, and Deeplabv3 + -to generate high-precision FVC in drylands with high-resolution (0.8 m) remote sensing images. Among these models, the Unet model performed better, with an accuracy of 93.38%, while the accuracy of Pspnet and Deeplabv3+ was 88.14% and 88.91%, respectively. Comparison with the FVC derived from normalized difference vegetation index (NDVI), and land use/land cover data from ESRI and ESA indicated that the FVC map produced by Unet was more consistent with on-site field observations. Delving into drivers influencing dryland FVC, we found that groundwater depth plays a pivotal role compared to topographical and climatic variables. Specifically, when the groundwater depth exceeds -3 m, the probability of occurring high FVC is reduced to 50%. This study innovatively extracted the FVC of drylands with high vegetation spatial heterogeneity, which better solves the insufficient accuracy of the existing dataset, serves as a valuable reference for monitoring vegetation change, and facilitates more precise quantification of carbon storage.
英文关键词	Image segmentation Fractional vegetation coverage Arid region Ecological restoration Deep learning
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:001135055600001
WOS关键词	LOWER TARIM RIVER ; LOWER REACHES ; GROUNDWATER LEVEL ; CHINA ; CLIMATE ; SEGMENTATION ; RESTORATION ; FRACTION ; FOREST ; TREES
WOS类目	Ecology
WOS研究方向	Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/403430
推荐引用方式 GB/T 7714	Wang, Haolin,Gui, Dongwei,Liu, Qi,et al. Vegetation coverage precisely extracting and driving factors analysis in drylands[J],2024,79.
APA	Wang, Haolin.,Gui, Dongwei.,Liu, Qi.,Feng, Xinlong.,Qu, Jia.,...&Wei, Guanghui.(2024).Vegetation coverage precisely extracting and driving factors analysis in drylands.ECOLOGICAL INFORMATICS,79.
MLA	Wang, Haolin,et al."Vegetation coverage precisely extracting and driving factors analysis in drylands".ECOLOGICAL INFORMATICS 79(2024).