Probabilistic assessment of remote sensing-based terrestrial vegetation vulnerability to drought stress of the Loess Plateau in China

doi:10.1016/j.rse.2019.111290

	Probabilistic assessment of remote sensing-based terrestrial vegetation vulnerability to drought stress of the Loess Plateau in China
	Fang, Wei 2; Huang, Shengzhi 2; Huang, Qiang 2; Huang, Guohe 3; Wang, Hao 4; Leng, Guoyong 1,5; Wang, Lu 2; Guo, Yi 2
刊名	REMOTE SENSING OF ENVIRONMENT
	2019-10-01
卷号	232 页码:22
关键词	Vegetation health Drought stress Copula method Conditional probability Vulnerability analysis
ISSN号	0034-4257
DOI	10.1016/j.rse.2019.111290
通讯作者	Huang, Shengzhi(huangshengzhi@xaut.edu.cn)
英文摘要	Quantitative understanding of vegetation vulnerability under drought stress is essential to initiating drought preparedness and mitigation. In this study, a bivariate probabilistic framework is developed for assessing vegetation vulnerability and mapping drought-prone ecosystems more informatively, which is different from previous studies conducted in a deterministic way. The Normalized Difference Vegetation Index (NDVI) is initially correlated to the Standardized Precipitation Index (SPI) at contrasting timescales to evaluate the degree of vegetation dependence on water availability and screen out the vegetation response time. Afterward, the monthly NDVI series is connected with the most correlated SPI to derive joint distributions using a copula method. On such basis, conditional probabilities of vegetation losses are estimated under multiple drought scenarios and used for revealing tempo-spatial patterns of vegetation vulnerability. Particular focus is directed to the Loess Plateau (LP), China, which is a world-famous environmentally fragile area. Results indicate that the proposed framework is valid for vegetation vulnerability assessment as the pair-wise SPI-NDVI observations fall within high-density areas of the estimated NDVI distributions. From a probabilistic perspective, roughly 95% of the LP exhibits greater probability of vegetation losses when suffering from water deficits rather than water surplus. Vegetation loss probabilities reaching their peak (39.7%) in summer indicate the highest vegetation vulnerability to drought stress in summer months sequentially followed by autumn (32.9%) and spring (31.0%), which is linked to marked variations in water requirement at different stages of vegetation growth. Spatially, drought-vulnerable regions are identified in the western edge with vegetation loss probability 20.6% higher than the LP mean value, suggesting higher vulnerability in more arid areas. Irrigation practices and large-scale vegetation restoration, as two important sources of anthropogenic disturbance in the LP, benefit the decreased vegetation vulnerability over the majority of affected areas. Results may increase our knowledge about climatic controls on vegetation health and support the ecosystem restoration planning in the LP.
资助项目	Belt and Road Special Foundation of the State Key Laboratory of Hydrology Water Resources and Hydraulic Engineering[2018490711]
WOS关键词	STANDARDIZED PRECIPITATION INDEX ; CLIMATE-CHANGE ; SOIL-MOISTURE ; ECOLOGICAL RESTORATION ; METEOROLOGICAL DROUGHT ; AGRICULTURAL DROUGHT ; RIPARIAN VEGETATION ; ECO-ENVIRONMENT ; CARBON BALANCE ; WATER DEMAND
WOS研究方向	Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology
语种	英语
出版者	ELSEVIER SCIENCE INC
WOS记录号	WOS:000486355300026
资助机构	Belt and Road Special Foundation of the State Key Laboratory of Hydrology Water Resources and Hydraulic Engineering
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/129612]
专题	中国科学院地理科学与资源研究所
通讯作者	Huang, Shengzhi
作者单位	1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China 2.Xian Univ Technol, Sch Water Resources & Hydropower, State Key Lab Ecohydraul Northwest Arid Reg China, Xian 710048, Shaanxi, Peoples R China 3.Univ Regina, Inst Energy Environm & Sustainable Communities, Regina, SK S4S 0A2, Canada 4.China Inst Water Resources & Hydropower Res, State Key Lab Simulat & Regulat Water Cycle River, Beijing 100038, Peoples R China 5.Univ Oxford, Environm Change Inst, Oxford OX1 3QY, England
推荐引用方式 GB/T 7714	Fang, Wei,Huang, Shengzhi,Huang, Qiang,et al. Probabilistic assessment of remote sensing-based terrestrial vegetation vulnerability to drought stress of the Loess Plateau in China[J]. REMOTE SENSING OF ENVIRONMENT,2019,232:22.
APA	Fang, Wei.,Huang, Shengzhi.,Huang, Qiang.,Huang, Guohe.,Wang, Hao.,...&Guo, Yi.(2019).Probabilistic assessment of remote sensing-based terrestrial vegetation vulnerability to drought stress of the Loess Plateau in China.REMOTE SENSING OF ENVIRONMENT,232,22.
MLA	Fang, Wei,et al."Probabilistic assessment of remote sensing-based terrestrial vegetation vulnerability to drought stress of the Loess Plateau in China".REMOTE SENSING OF ENVIRONMENT 232(2019):22.