干旱区生态环境知识资源中心(Arid): Multi-Dimensional Evaluation of Ecosystem Health in China's Loess Plateau Based on Function-Oriented Metrics and BFAST Algorithm

Arid

DOI	10.3390/rs15020383
	Multi-Dimensional Evaluation of Ecosystem Health in China's Loess Plateau Based on Function-Oriented Metrics and BFAST Algorithm
	Li, Xiaoyue; Liu, Xiangnan; Hou, Bowen; Tian, Lingwen; Yang, Qin; Zhu, Lihong; Meng, Yuanyuan
通讯作者	Liu, XN
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2023
卷号	15 期号:2
英文摘要	China's Loess Plateau (CLP) is a typical semi-arid region and is very sensitive to climate and human activity. Under the ecological restoration project, vegetation coverage increased significantly, but the limitation of climate and other factors has meant that vegetation mortality was relatively high. Therefore, it is of great significance to evaluate the ecosystem health in the CLP in terms of the sustainability of ecological restoration projects. The aim of this study is to propose a multi-dimensional assessment method to investigate vegetation health changes in the CLP based on BFAST and BFAST01 algorithms. To achieve this, we constructed local dimension health indexes (the number of disturbances and recovery rate) and overall dimension health indexes (trend types) based on the gross primary productivity (GPP) and vegetation evapotranspiration (Ec) data of the study area from 2001 to 2020 which was collected from the Google Earth Engine (GEE) platform. The result revealed the following. More than 90% of disturbance pixels of GPP and Ec in the short-term change only once and more than 60% of pixels recover after disturbance. However, the recovery rate after disturbance is slow, and the interval with the largest proportion is 0-0.00015. The long-term trend mostly exhibited a monotonic increasing trend. These results indicate that the function of the ecosystem on the CLP has been improved, but the resilience of vegetation is weak. In conclusion, the combination of the local dimension and overall dimension analysis can comprehensively reveal information about the CLP's vegetation health in the past two decades, and that the method will open new perspectives and generate new knowledge about vegetation health in the CLP.
英文关键词	China's Loess Plateau ecosystem health BFAST algorithm gross primary productivity vegetation evapotranspiration
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:000926172500001
WOS关键词	GROSS PRIMARY PRODUCTION ; NORTHERN SHAANXI ; LAND-COVER ; VEGETATION ; TRENDS ; EVAPOTRANSPIRATION ; CLIMATE ; GRAIN
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/2XILL650/398227
推荐引用方式 GB/T 7714	Li, Xiaoyue,Liu, Xiangnan,Hou, Bowen,et al. Multi-Dimensional Evaluation of Ecosystem Health in China's Loess Plateau Based on Function-Oriented Metrics and BFAST Algorithm[J],2023,15(2).
APA	Li, Xiaoyue.,Liu, Xiangnan.,Hou, Bowen.,Tian, Lingwen.,Yang, Qin.,...&Meng, Yuanyuan.(2023).Multi-Dimensional Evaluation of Ecosystem Health in China's Loess Plateau Based on Function-Oriented Metrics and BFAST Algorithm.REMOTE SENSING,15(2).
MLA	Li, Xiaoyue,et al."Multi-Dimensional Evaluation of Ecosystem Health in China's Loess Plateau Based on Function-Oriented Metrics and BFAST Algorithm".REMOTE SENSING 15.2(2023).