Application of remote sensing techniques and machine learning algorithms in dust source detection and dust source susceptibility mapping

Summary, in English

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.

• Author: Mahdi Boroughani; Sima Pourhashemi; Hossein Hashemi; Mahdi Salehi; Abolghasem Amirahmadi; Mohammad Ali Zangane Asadi; Ronny Berndtsson
• Department/s: Middle Eastern Studies; Centre for Advanced Middle Eastern Studies (CMES); Division of Water Resources Engineering; LTH Profile Area: Water; MECW: The Middle East in the Contemporary World
• Publishing year: 2020-03
• Language: English
• Publication/Series: Ecological Informatics
• Volume: 56
• Links: http://dx.doi.org/10.1016/j.ecoinf.2020.101059
• Document type: Journal article
• Publisher: Elsevier
• Topic: Signal Processing
• Keywords: Dust source; Frequency ratio (FR); Iran; Random Forest (RF); Remote sensing; Weights of evidence (WOE)
• Status: Published
• ISBN/ISSN/Other: ISSN: 1574-9541