干旱区生态环境知识资源中心(Arid): Leveraging Google Earth Engine platform to characterize and map small seasonal wetlands in the semi-arid environments of South Africa

Arid

DOI	10.1016/j.scitotenv.2021.150139
	Leveraging Google Earth Engine platform to characterize and map small seasonal wetlands in the semi-arid environments of South Africa
	Gxokwe, Siyamthanda; Dube, Timothy; Mazvimavi, Dominic
通讯作者	Gxokwe, S (corresponding author), Univ Western Cape, Inst Water Studies, Dept Earth Sci, Private Bag X17, ZA-7535 Cape Town, South Africa.
来源期刊	SCIENCE OF THE TOTAL ENVIRONMENT
ISSN	0048-9697
EISSN	1879-1026
出版年	2022
卷号	803
英文摘要	Although significant scientific research strides have been made in mapping the spatial extents and ecohydrological dynamics of wetlands in semi-arid environments, the focus on small wetlands remains a challenge. This is due to the sensing characteristics of remote sensing platforms and lack of robust data processing techniques. Advancements in data analytic tools, such as the introduction of Google Earth Engine (GEE) platform provides unique opportunities for improved assessment of small and scattered wetlands. This study thus assessed the capabilities of GEE cloud-computing platform in characterising small seasonal flooded wetlands, using the new generation Sentinel 2 data from 2016 to 2020. Specifically, the study assessed the spectral separability of different land cover classes for two different wetlands detected, using Sentinel-2 multi-year composite water and vegetation indices and to identify the most suitable GEE machine learning algorithm for accurately detecting and mapping semi-arid seasonal wetlands. This was achieved using the object based Random Forest (RF), Support Vector Machine (SVM), Classification and Regression Tree (CART) and Naive Bayes (NB) advanced algorithms in GEE. The results demonstrated the capabilities of using the GEE platform to characterize wetlands with acceptable accuracy. All algorithms showed superiority, in mapping the two wetlands except for the NB method, which had lowest overall classification accuracy. These findings underscore the relevance of the GEE platform, Sentinel-2 data and advanced algorithms in characterizing small and seasonal semi-arid wetlands. (c) 2021 Elsevier B.V. All rights reserved.
英文关键词	Limpopo River Basin Object-based classification Machine learning algorithm Wetland mapping Wetland condition
类型	Article
语种	英语
开放获取类型	Green Submitted
收录类别	SCI-E
WOS记录号	WOS:000702845300008
WOS关键词	RANDOM FOREST CLASSIFIER ; BIG DATA APPLICATIONS ; LIMPOPO RIVER-BASIN ; WATER ; AREAS
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/2XILL650/364610
作者单位	[Gxokwe, Siyamthanda; Dube, Timothy; Mazvimavi, Dominic] Univ Western Cape, Inst Water Studies, Dept Earth Sci, Private Bag X17, ZA-7535 Cape Town, South Africa
推荐引用方式 GB/T 7714	Gxokwe, Siyamthanda,Dube, Timothy,Mazvimavi, Dominic. Leveraging Google Earth Engine platform to characterize and map small seasonal wetlands in the semi-arid environments of South Africa[J],2022,803.
APA	Gxokwe, Siyamthanda,Dube, Timothy,&Mazvimavi, Dominic.(2022).Leveraging Google Earth Engine platform to characterize and map small seasonal wetlands in the semi-arid environments of South Africa.SCIENCE OF THE TOTAL ENVIRONMENT,803.
MLA	Gxokwe, Siyamthanda,et al."Leveraging Google Earth Engine platform to characterize and map small seasonal wetlands in the semi-arid environments of South Africa".SCIENCE OF THE TOTAL ENVIRONMENT 803(2022).