干旱区生态环境知识资源中心(Arid): Spatiotemporal Assessment of Forest Fire Vulnerability in China Using Automated Machine Learning

Arid

DOI	10.3390/rs14235965
	Spatiotemporal Assessment of Forest Fire Vulnerability in China Using Automated Machine Learning
	Ren, Hongge; Zhang, Li; Yan, Min; Chen, Bowei; Yang, Zhenyu; Ruan, Linlin
通讯作者	Yan, M
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2022
卷号	14 期号:23
英文摘要	Frequent forest fires cause air pollution, threaten biodiversity and spoil forest ecosystems. Forest fire vulnerability assessment is a potential way to improve the ability of forests to resist climate disasters and help formulate appropriate forest management countermeasures. Here, we developed an automated hybrid machine learning algorithm by selecting the optimal model from 24 models to map potential forest fire vulnerability over China during the period 2001-2020. The results showed forest aboveground biomass (AGB) had a vulnerability of 26%, indicating that approximately 2.32 Gt C/year of forest AGB could be affected by fire disturbances. The spatiotemporal patterns of forest fire vulnerability were dominated by both forest characteristics and climate conditions. Hotspot regions for vulnerability were mainly located in arid areas in western China, mountainous areas in southwestern China, and edges of vegetation zones. The overall forest fire vulnerability across China was insignificant. The forest fire vulnerability of boreal and temperate coniferous forests and mixed forests showed obviously decreasing trends, and cultivated forests showed an increasing trend. The results of this study are expected to provide important support for the forest ecosystem management in China.
英文关键词	forest fire vulnerability automated machine learning aboveground biomass (AGB) spatiotemporal patterns
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:000896210000001
WOS关键词	CLIMATE-CHANGE ; CARBON ; PATTERNS
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/394239
推荐引用方式 GB/T 7714	Ren, Hongge,Zhang, Li,Yan, Min,et al. Spatiotemporal Assessment of Forest Fire Vulnerability in China Using Automated Machine Learning[J],2022,14(23).
APA	Ren, Hongge,Zhang, Li,Yan, Min,Chen, Bowei,Yang, Zhenyu,&Ruan, Linlin.(2022).Spatiotemporal Assessment of Forest Fire Vulnerability in China Using Automated Machine Learning.REMOTE SENSING,14(23).
MLA	Ren, Hongge,et al."Spatiotemporal Assessment of Forest Fire Vulnerability in China Using Automated Machine Learning".REMOTE SENSING 14.23(2022).