干旱区生态环境知识资源中心(Arid): Vegetation Types Mapping Using Multi-Temporal Landsat Images in the Google Earth Engine Platform

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DOI	10.3390/rs13224683
	Vegetation Types Mapping Using Multi-Temporal Landsat Images in the Google Earth Engine Platform
	Aghababaei, Masoumeh; Ebrahimi, Ataollah; Naghipour, Ali Asghar; Asadi, Esmaeil; Verrelst, Jochem
通讯作者	Ebrahimi, A (corresponding author), Shahrekord Univ, Fac Nat Resources & Earth Sci, Dept Range & Watershed Management, Shahrekord 8818634141, Iran.
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2021
卷号	13 期号:22
英文摘要	Vegetation Types (VTs) are important managerial units, and their identification serves as essential tools for the conservation of land covers. Despite a long history of Earth observation applications to assess and monitor land covers, the quantitative detection of sparse VTs remains problematic, especially in arid and semiarid areas. This research aimed to identify appropriate multi-temporal datasets to improve the accuracy of VTs classification in a heterogeneous landscape in Central Zagros, Iran. To do so, first the Normalized Difference Vegetation Index (NDVI) temporal profile of each VT was identified in the study area for the period of 2018, 2019, and 2020. This data revealed strong seasonal phenological patterns and key periods of VTs separation. It led us to select the optimal time series images to be used in the VTs classification. We then compared single-date and multi-temporal datasets of Landsat 8 images within the Google Earth Engine (GEE) platform as the input to the Random Forest classifier for VTs detection. The single-date classification gave a median Overall Kappa (OK) and Overall Accuracy (OA) of 51% and 64%, respectively. Instead, using multi-temporal images led to an overall kappa accuracy of 74% and an overall accuracy of 81%. Thus, the exploitation of multi-temporal datasets favored accurate VTs classification. In addition, the presented results underline that available open access cloud-computing platforms such as the GEE facilitates identifying optimal periods and multitemporal imagery for VTs classification.
英文关键词	vegetation types classification multi-temporal images machine learning Google Earth Engine NDVI
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:000728602500001
WOS关键词	CLASSIFICATION ; PERFORMANCE ; SENTINEL-2
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/2XILL650/373699
作者单位	[Aghababaei, Masoumeh; Ebrahimi, Ataollah; Naghipour, Ali Asghar; Asadi, Esmaeil] Shahrekord Univ, Fac Nat Resources & Earth Sci, Dept Range & Watershed Management, Shahrekord 8818634141, Iran; [Verrelst, Jochem] Univ Valencia, Image Proc Lab IPL, Parc Cient, Paterna 46980, Spain
推荐引用方式 GB/T 7714	Aghababaei, Masoumeh,Ebrahimi, Ataollah,Naghipour, Ali Asghar,et al. Vegetation Types Mapping Using Multi-Temporal Landsat Images in the Google Earth Engine Platform[J],2021,13(22).
APA	Aghababaei, Masoumeh,Ebrahimi, Ataollah,Naghipour, Ali Asghar,Asadi, Esmaeil,&Verrelst, Jochem.(2021).Vegetation Types Mapping Using Multi-Temporal Landsat Images in the Google Earth Engine Platform.REMOTE SENSING,13(22).
MLA	Aghababaei, Masoumeh,et al."Vegetation Types Mapping Using Multi-Temporal Landsat Images in the Google Earth Engine Platform".REMOTE SENSING 13.22(2021).