干旱区生态环境知识资源中心(Arid): Multi-model assessment of potential natural vegetation to support ecological restoration

Arid

DOI	10.1016/j.jenvman.2024.121934
	Multi-model assessment of potential natural vegetation to support ecological restoration
	Ci, Mengtao; Liu, Qi; Liu, Yunfei; Jin, Qian; Martinez-Valderrama, Jaime; Zhao, Jianping
通讯作者	Liu, Q
来源期刊	JOURNAL OF ENVIRONMENTAL MANAGEMENT
ISSN	0301-4797
EISSN	1095-8630
出版年	2024
卷号	367
英文摘要	Ecological restoration is imperative for controlling desertification. Potential natural vegetation (PNV), the theoretical vegetation succession state, can guides near-natural restoration. Although a rising transition from traditional statistical methods to advanced machine learning and deep learning is observed in PNV simulation, a comprehensive comparison of their performance is still unexplored. Therefore, we overview the performance of PNV mapping in terms of 12 commonly used methods with varying spatial scales and sample sizes. Our findings indicate that the methodology should be carefully selected due to the variation in performance of different model types, with Area Under the Curve (AUC) values ranging from 0.65 to 0.95 for models with sample sizes up to 80% of the total sample size. Specifically, semi-supervised learning performs best with small sample sizes (i.e., 10 to 200), while Random Forest, XGBoost, and artificial neural networks perform better with large sample sizes (i.e., over 500). Further, the performance of all models tends to improve significantly as the sample size increases and the grain size of the crystals becomes smaller. Take the downstream Tarim River Basin, a hyper-arid region undergoing ecological restoration, as a case study. We showed that its potential restored areas were overestimated by 2-3 fold as the spatial scale became coarser, revealing the caution needed while planning restoration projects at coarse resolution. These findings enhance the application of PNV in the design of restoration programs to prevent desertification.
英文关键词	Desertification control Environmental management Afforestation Multi-model assessment Artificial intelligence
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001286606100001
WOS关键词	SPECIES DISTRIBUTION MODELS ; DESERT RIPARIAN FOREST ; CLIMATE-CHANGE ; SAMPLE-SIZE ; CHINA ; DISTRIBUTIONS ; SCALE ; DESERTIFICATION ; PERFORMANCE ; ECOSYSTEMS
WOS类目	Environmental Sciences
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/2XILL650/404492
推荐引用方式 GB/T 7714	Ci, Mengtao,Liu, Qi,Liu, Yunfei,et al. Multi-model assessment of potential natural vegetation to support ecological restoration[J],2024,367.
APA	Ci, Mengtao,Liu, Qi,Liu, Yunfei,Jin, Qian,Martinez-Valderrama, Jaime,&Zhao, Jianping.(2024).Multi-model assessment of potential natural vegetation to support ecological restoration.JOURNAL OF ENVIRONMENTAL MANAGEMENT,367.
MLA	Ci, Mengtao,et al."Multi-model assessment of potential natural vegetation to support ecological restoration".JOURNAL OF ENVIRONMENTAL MANAGEMENT 367(2024).