干旱区生态环境知识资源中心(Arid): Performance of ANN, SVM and MLH techniques for land use/cover change detection at Sultan Marshes wetland, Turkey

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DOI	10.2166/wst.2019.290
	Performance of ANN, SVM and MLH techniques for land use/cover change detection at Sultan Marshes wetland, Turkey
	Kesikoglu, M. Hayri; Atasever, U. Haluk; Dadaser-Celik, Filiz; Ozkan, Coskun
通讯作者	Kesikoglu, MH (corresponding author), Erciyes Univ, Dept Geomat Engn, TR-38039 Kayseri, Turkey.
来源期刊	WATER SCIENCE AND TECHNOLOGY
ISSN	0273-1223
EISSN	1996-9732
出版年	2019
卷号	80 期号:3 页码:466-477
英文摘要	Wetlands are among the most productive ecosystems that provide services ranging from flood control to climate change mitigation. Wetlands are also critical habitats for the survival of numerous plant and animal species. In this study, we used satellite remote sensing techniques for classification and change detection at an internationally important wetland (Ramsar Site) in Turkey. Sultan Marshes is located at the center of semi-arid Develi closed basin. The wetlands have undergone significant changes since the 1980s due to changes in water flow regimes, but changes in recent years have not been sufficiently explored yet. In this study, we focused on the changes from 2005 to 2012. Two multispectral ASTER images with spatial resolution of 15 m, acquired on June 11, 2005 and May 20, 2012, were used in the analyses. After geometric correction, the images were classified into four information classes, namely water, marsh, agriculture, and steppe. The applicability of three classification methods (i.e. maximum likelihood (MLH), multi-layer perceptron type artificial neural networks (ANN) and support vector machines (SVM)) was assessed. The differences in classification accuracies were evaluated by the McNemar's test. The changes in the Sultan Marshes were determined by the post classification comparison method using the most accurate classified images. The results showed that the highest overall accuracy in image classifications was achieved with the SVM method. It was observed that marshes and steppe areas decreased while water and agricultural areas expanded from 2005 to 2012. These changes could be the results of water transfers to the marshes from neighboring watershed.
英文关键词	artificial neural networks change detection maximum likelihood Sultan Marshes support vector machines wetlands
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000503483500008
WOS关键词	ECOSYSTEM SERVICES ; NEURAL-NETWORK ; CLASSIFICATION ; COVER
WOS类目	Engineering, Environmental ; Environmental Sciences ; Water Resources
WOS研究方向	Engineering ; Environmental Sciences & Ecology ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/369545
作者单位	[Kesikoglu, M. Hayri; Atasever, U. Haluk; Ozkan, Coskun] Erciyes Univ, Dept Geomat Engn, TR-38039 Kayseri, Turkey; [Dadaser-Celik, Filiz] Erciyes Univ, Dept Environm Engn, TR-38039 Kayseri, Turkey
推荐引用方式 GB/T 7714	Kesikoglu, M. Hayri,Atasever, U. Haluk,Dadaser-Celik, Filiz,et al. Performance of ANN, SVM and MLH techniques for land use/cover change detection at Sultan Marshes wetland, Turkey[J],2019,80(3):466-477.
APA	Kesikoglu, M. Hayri,Atasever, U. Haluk,Dadaser-Celik, Filiz,&Ozkan, Coskun.(2019).Performance of ANN, SVM and MLH techniques for land use/cover change detection at Sultan Marshes wetland, Turkey.WATER SCIENCE AND TECHNOLOGY,80(3),466-477.
MLA	Kesikoglu, M. Hayri,et al."Performance of ANN, SVM and MLH techniques for land use/cover change detection at Sultan Marshes wetland, Turkey".WATER SCIENCE AND TECHNOLOGY 80.3(2019):466-477.