干旱区生态环境知识资源中心(Arid): Determining the response of riparian vegetation and river morphology to drought using Google Earth Engine and machine learning

Arid

DOI	10.1016/j.jaridenv.2023.105068
	Determining the response of riparian vegetation and river morphology to drought using Google Earth Engine and machine learning
	Chaulagain, Smriti; Stone, Mark C.; Morrison, Ryan R.; Yang, Liping; Coonrod, Julie; Villa, Noelani E.
通讯作者	Chaulagain, S
来源期刊	JOURNAL OF ARID ENVIRONMENTS
ISSN	0140-1963
EISSN	1095-922X
出版年	2023
卷号	219
英文摘要	Riparian vegetation composition and channel morphology are susceptible to long-term alterations caused by external stressors, including climate-change-induced droughts and engineered infrastructures. The objectives of this study were to (1) quantify trends in riparian vegetation and channel/floodplain morphology over large spatial (-290 km) and temporal scales (-30 years) and (2) investigate the relationships between hydroclimatic drivers and changes in riparian vegetation and channel morphology. We implemented a random forest classifier via a machine learning technique in Google Earth Engine. The study area was a 290 km reach of the Rio Grande located in New Mexico, USA. We used the combination of remotely sensed data and products (e.g., Landsat imagery, Normalized Difference Vegetation Index (NDVI), and land cover) to characterize vegetation, vegetation cover changes, and river morphology shifts from 1984 to 2020. The trend analysis revealed increased vegetated areas and NDVI (0.0004/yr) during long-term drought. The channel experienced a reduction in width associated with vegetation encroachment and the formation of stable vegetated islands. The streamflow hydrograph characteristics were positively correlated with vegetation cover and channel morphology. Our study contributes novel insights into the long-term riparian ecosystem dynamics under drought stress, informing drought impact mitigation and ecosystem management in arid and semi-arid regions.
英文关键词	Riparian vegetation River morphology Remote sensing Random forest Rio Grande Drought
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001082728000001
WOS关键词	CHANNEL EVOLUTION ; DYNAMICS ; CLIMATE ; ISLAND
WOS类目	Ecology ; 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/2XILL650/397161
推荐引用方式 GB/T 7714	Chaulagain, Smriti,Stone, Mark C.,Morrison, Ryan R.,et al. Determining the response of riparian vegetation and river morphology to drought using Google Earth Engine and machine learning[J],2023,219.
APA	Chaulagain, Smriti,Stone, Mark C.,Morrison, Ryan R.,Yang, Liping,Coonrod, Julie,&Villa, Noelani E..(2023).Determining the response of riparian vegetation and river morphology to drought using Google Earth Engine and machine learning.JOURNAL OF ARID ENVIRONMENTS,219.
MLA	Chaulagain, Smriti,et al."Determining the response of riparian vegetation and river morphology to drought using Google Earth Engine and machine learning".JOURNAL OF ARID ENVIRONMENTS 219(2023).