干旱区生态环境知识资源中心(Arid): Crop type detection using an object-based classification method and multi-temporal Landsat satellite images

Arid

DOI	10.1007/s10333-022-00901-x
	Crop type detection using an object-based classification method and multi-temporal Landsat satellite images
	Karimi, Neamat; Sheshangosht, Sara; Eftekhari, Mortaza
通讯作者	Karimi, N
来源期刊	PADDY AND WATER ENVIRONMENT
ISSN	1611-2490
EISSN	1611-2504
出版年	2022
卷号	20 期号:3 页码:395-412
英文摘要	Crop type detection is of great importance in water resource allocation and planning mostly in arid and semi-arid regions of Iran. Landsat-OLI 16-day inter-annual images are invaluable sources obviating crop monitoring into issues of crop types detection, crop yield prediction, and crop pattern studies. Although many classification methods such as decision tree (DT), support vector machine (SVM), and maximum likelihood (ML) were implied for crop type mapping, recent researches often use an object-based classification approach. In this study, an object-based image analysis (OBIA) classifier based on rule-based decision tree (RBDT) and object-based nearest neighbor (OBNN) used to delineate five common crop types (includes Wheat and Barley together in one class, rice, multiple crop (MC), Alfalfa and Spring crops) in Isfahan city and nearby areas. The classification was applied in five scenarios using different vegetation indexes including normalized difference vegetation index (NDVI), normalized difference water index (NDWI), green normalized difference vegetation index GNDVI and their combination. All scenarios property and accuracy assessed both with by class separation distance matrix and confusion matrix. The overall accuracy of classification with using only one vegetation index was lower than other scenarios. It was the lowest for GNDVI rating 37% whereas combination of Indexes resulted better accuracy. In final map with combination of NDVI, GNDVI and NDWI, overall accuracy and kappa achieved to 88% and 0/83 successively. Comparing individual accuracy of different crops showed that MC crops with 66% has the lowest accuracy and Wheat-Barely crops with 94.8% individual accuracy has the Maximum accuracy. Other crop types accuracy alters between 66 and 94.8%.
英文关键词	Crop type Object based image analysis Segmentation NDVI NDWI
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000795513100001
WOS关键词	DIFFERENCE WATER INDEX ; TIME-SERIES ; SENTINEL-2 DATA ; COVER ; AGRICULTURE ; NDWI
WOS类目	Agricultural Engineering ; Agronomy
WOS研究方向	Agriculture
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/393892
推荐引用方式 GB/T 7714	Karimi, Neamat,Sheshangosht, Sara,Eftekhari, Mortaza. Crop type detection using an object-based classification method and multi-temporal Landsat satellite images[J],2022,20(3):395-412.
APA	Karimi, Neamat,Sheshangosht, Sara,&Eftekhari, Mortaza.(2022).Crop type detection using an object-based classification method and multi-temporal Landsat satellite images.PADDY AND WATER ENVIRONMENT,20(3),395-412.
MLA	Karimi, Neamat,et al."Crop type detection using an object-based classification method and multi-temporal Landsat satellite images".PADDY AND WATER ENVIRONMENT 20.3(2022):395-412.