干旱区生态环境知识资源中心(Arid): Identification of dust sources in a dust hot-spot area in Iran using multi-spectral Sentinel 2 data and deep learning artificial intelligence machine

Arid

DOI	10.1080/10106049.2022.2043452
	Identification of dust sources in a dust hot-spot area in Iran using multi-spectral Sentinel 2 data and deep learning artificial intelligence machine
	Dolatkordestani, Mojtaba; Nosrati, Kazem; Maddah, Saeid; Tiefenbacher, John P.
通讯作者	Dolatkordestani, M
来源期刊	GEOCARTO INTERNATIONAL
ISSN	1010-6049
EISSN	1752-0762
出版年	2022
卷号	37 期号:25 页码:10950-10969
英文摘要	The drying of wetlands in Iran due to climate change and indiscriminate human activities has increased dust production. Dust storms have become a major problem in arid and semi-arid regions and cause adverse social, economic, and environmental effects. The Jazmurian wetland in Kerman Province is one such area. To identify dust sources in the Jazmurian basin, high resolution Sentinel 2 data were used. From these, sediment supply was mapped. Three artificially intelligent algorithms-artificial neural network (ANN), support vector machine (SVM), and deep-learning neural network (DLNN)-were used to model dust-production potential in the study area. The results show that portions of the Jazmurian basin that have dried up in recent years have a very high potential for dust production. Evaluation of the models' performances using area-under-curve (AUC) statistics revealed that the DLNN model is more efficient (AUC = 0.97) than either the ANN (AUC = 0.91) or SVM (AUC = 0.92). All three models reveal that NDVI, elevation, annual rainfall, and windspeed are the four most important factors influencing dust-production potential in the study area. This remote sensing-artificial intelligence framework should be tested for mapping dust-production potential in other regions as this study demonstrates highly accurate, high-resolution results. This study yielded fundamental information to identify locations in need of desertification management and mitigation of dust production in the Jazmurian basin.
英文关键词	Deep-learning neural network Jazmurian basin dust-source Sentinel 2 desertification
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000801101500001
WOS关键词	STORM SOURCE AREAS ; MINERAL DUST ; EMISSION ; CLIMATE ; INDEX
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/2XILL650/392891
推荐引用方式 GB/T 7714	Dolatkordestani, Mojtaba,Nosrati, Kazem,Maddah, Saeid,et al. Identification of dust sources in a dust hot-spot area in Iran using multi-spectral Sentinel 2 data and deep learning artificial intelligence machine[J],2022,37(25):10950-10969.
APA	Dolatkordestani, Mojtaba,Nosrati, Kazem,Maddah, Saeid,&Tiefenbacher, John P..(2022).Identification of dust sources in a dust hot-spot area in Iran using multi-spectral Sentinel 2 data and deep learning artificial intelligence machine.GEOCARTO INTERNATIONAL,37(25),10950-10969.
MLA	Dolatkordestani, Mojtaba,et al."Identification of dust sources in a dust hot-spot area in Iran using multi-spectral Sentinel 2 data and deep learning artificial intelligence machine".GEOCARTO INTERNATIONAL 37.25(2022):10950-10969.