干旱区生态环境知识资源中心(Arid): Application of remote sensing techniques and machine learning algorithms in dust source detection and dust source susceptibility mapping

Arid

DOI	10.1016/j.ecoinf.2020.101059
	Application of remote sensing techniques and machine learning algorithms in dust source detection and dust source susceptibility mapping
	Boroughani, Mahdi 1,4; Pourhashemi, Sima 2,4; Hashemi, Hossein 3,4; Salehi, Mahdi 5; Amirahmadi, Abolghasem 2; Asadi, Mohammad Ali Zangane 2; Berndtsson, Ronny 3,4
通讯作者	Boroughani, Mahdi
来源期刊	ECOLOGICAL INFORMATICS
ISSN	1574-9541
EISSN	1878-0512
出版年	2020
卷号	56
英文摘要	The aim of this research was to develop a method to produce a Dust Source Susceptibility Map (DSSM). For this purpose, we applied remote sensing and statistical-based machine learning algorithms for experimental dust storm studies in the Khorasan Razavi Province, in north-eastern Iran. We identified dust sources in the study area using MODIS satellite images during the 2005-2016 period. For dust source identification, four indices encompassing BTD3132, BTD2931, NDDI, and D variable for 23 MODIS satellite images were calculated. As a result, 65 dust source points were identified, which were categorized into dust source data points for training and validation of the machine learning algorithms. Three statistical-based machine learning algorithms were used including Weights of Evidence (WOE), Frequency Ratio (FR), and Random Forest (RF) to produce DSSM for the study region. We used land use, lithology, slope, soil, geomorphology, NDVI (Normalized Difference Vegetation Index), and distance from river as conditioning variables in the modelling. To check the performance of the models, we applied the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC). As for the AUC success rate (training), the FR and WOE algorithms resulted in 82 and 83% accuracy, respectively, while the RF algorithm resulted in 91% accuracy. As for the AUC predictive rate (validation), the accuracy of all three models, FR, WOE, and RF, were 80, 81, and 88%, respectively. Although all three algorithms produced acceptable susceptibility maps of dust sources, the results indicated better performance of the RF algorithm.
英文关键词	Dust source Frequency ratio (FR) Weights of evidence (WOE) Random Forest (RF) Remote sensing Iran
类型	Article
语种	英语
国家	Iran ; Sweden
收录类别	SCI-E
WOS记录号	WOS:000519656700014
WOS关键词	LOGISTIC-REGRESSION ; RANDOM FOREST ; OF-EVIDENCE ; FREQUENCY RATIO ; DESERT DUST ; 3 GORGES ; STORM ; GIS ; IDENTIFICATION ; WEIGHT
WOS类目	Ecology
WOS研究方向	Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/314363
作者单位	1.Hak Sabzevari Univ, Res Ctr Geosci & Social Studies, Sabzevar, Iran; 2.Hakim Sabzevari Univ, Dept Geog, Sabzevar, Iran; 3.Lund Univ, Dept Water Resources Engn, Lund, Sweden; 4.Lund Univ, Ctr Middle Eastern Studies, Lund, Sweden; 5.Univ Neyshabur, Dept Math & Stat, Neyshabur, Iran
推荐引用方式 GB/T 7714	Boroughani, Mahdi,Pourhashemi, Sima,Hashemi, Hossein,et al. Application of remote sensing techniques and machine learning algorithms in dust source detection and dust source susceptibility mapping[J],2020,56.
APA	Boroughani, Mahdi.,Pourhashemi, Sima.,Hashemi, Hossein.,Salehi, Mahdi.,Amirahmadi, Abolghasem.,...&Berndtsson, Ronny.(2020).Application of remote sensing techniques and machine learning algorithms in dust source detection and dust source susceptibility mapping.ECOLOGICAL INFORMATICS,56.
MLA	Boroughani, Mahdi,et al."Application of remote sensing techniques and machine learning algorithms in dust source detection and dust source susceptibility mapping".ECOLOGICAL INFORMATICS 56(2020).