干旱区生态环境知识资源中心(Arid): Application of random forest classification and remotely sensed data in geological mapping on the Jebel Meloussi area (Tunisia)

Arid

DOI	10.1007/s12517-021-08509-x
	Application of random forest classification and remotely sensed data in geological mapping on the Jebel Meloussi area (Tunisia)
	Albert, Gaspar; Ammar, Seif
通讯作者	Albert, G (corresponding author), Eotvos Lorand Univ, H-1117 Budapest, Hungary.
来源期刊	ARABIAN JOURNAL OF GEOSCIENCES
ISSN	1866-7511
EISSN	1866-7538
出版年	2021
卷号	14 期号:21
英文摘要	Remotely sensed data such as satellite photos and radar images can be used to produce geological maps on arid regions, where the vegetation coverage does not have a significant effect. In central Tunisia, the Jebel Meloussi area has unique geological features and characteristic morphology (i.e. flat areas with dune fields in contrast with hills of folded and eroded stratigraphic sequences), which makes it an ideal area for testing new methods of automatic terrain classification. For this, data from the Sentinel 2 satellite sensor and the SRTM-based MERIT DEM (digital elevation model) were used in the present study. Using R scripts and the random forest classification method, modelling was performed on four lithological variables-derived from the different bands of the Sentinel 2 images-and two morphometric parameters for the area of the 1:50,000 geological map sheet no. 103. The four lithological variables were chosen to highlight the iron-bearing minerals since the spectral parameters of the Sentinel 2 sensors are especially useful for this purpose. The training areas of the classification were selected on the geological map. The results of the modelling identified Eocene and Cretaceous evaporite-bearing sedimentary series (such as the Jebs and the Bouhedma Formations) with the highest producer accuracy (> 60% of the predicted pixels match with the map). The pyritic argillites of the Sidi Khalif Formation were also recognized with the same accuracy, and the Quaternary sebhkas and dunes were also well predicted. The study concludes that the classification-based geological map is useful for field geologist prior to field surveys.
英文关键词	Geological mapping Sentinel 2 Remote sensing Random forest classification Machine learning
类型	Article
语种	英语
开放获取类型	hybrid
收录类别	SCI-E
WOS记录号	WOS:000712863900004
WOS关键词	VALIDATION ; ALGORITHMS ; LITHOLOGY
WOS类目	Geosciences, Multidisciplinary
WOS研究方向	Geology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/368232
作者单位	[Albert, Gaspar] Eotvos Lorand Univ, H-1117 Budapest, Hungary; [Ammar, Seif] ELTE Doctorate Sch Earth Sci, H-1117 Budapest, Hungary
推荐引用方式 GB/T 7714	Albert, Gaspar,Ammar, Seif. Application of random forest classification and remotely sensed data in geological mapping on the Jebel Meloussi area (Tunisia)[J],2021,14(21).
APA	Albert, Gaspar,&Ammar, Seif.(2021).Application of random forest classification and remotely sensed data in geological mapping on the Jebel Meloussi area (Tunisia).ARABIAN JOURNAL OF GEOSCIENCES,14(21).
MLA	Albert, Gaspar,et al."Application of random forest classification and remotely sensed data in geological mapping on the Jebel Meloussi area (Tunisia)".ARABIAN JOURNAL OF GEOSCIENCES 14.21(2021).