Skip to main content

Lund University Publications

LUND UNIVERSITY LIBRARIES

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

Boroughani, Mahdi LU ; Pourhashemi, Sima LU ; Hashemi, Hossein LU orcid ; Salehi, Mahdi ; Amirahmadi, Abolghasem ; Asadi, Mohammad Ali Zangane and Berndtsson, Ronny LU orcid (2020) In Ecological Informatics 56.
Abstract

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... (More)

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.

(Less)
Please use this url to cite or link to this publication:
author
; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Dust source, Frequency ratio (FR), Iran, Random Forest (RF), Remote sensing, Weights of evidence (WOE)
in
Ecological Informatics
volume
56
article number
101059
publisher
Elsevier
external identifiers
  • scopus:85078774663
ISSN
1574-9541
DOI
10.1016/j.ecoinf.2020.101059
language
English
LU publication?
yes
id
dfc22150-8c09-4e1b-9a6e-8c746d7681a3
date added to LUP
2020-02-11 12:25:26
date last changed
2023-10-08 00:25:03
@article{dfc22150-8c09-4e1b-9a6e-8c746d7681a3,
  abstract     = {{<p>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.</p>}},
  author       = {{Boroughani, Mahdi and Pourhashemi, Sima and Hashemi, Hossein and Salehi, Mahdi and Amirahmadi, Abolghasem and Asadi, Mohammad Ali Zangane and Berndtsson, Ronny}},
  issn         = {{1574-9541}},
  keywords     = {{Dust source; Frequency ratio (FR); Iran; Random Forest (RF); Remote sensing; Weights of evidence (WOE)}},
  language     = {{eng}},
  publisher    = {{Elsevier}},
  series       = {{Ecological Informatics}},
  title        = {{Application of remote sensing techniques and machine learning algorithms in dust source detection and dust source susceptibility mapping}},
  url          = {{http://dx.doi.org/10.1016/j.ecoinf.2020.101059}},
  doi          = {{10.1016/j.ecoinf.2020.101059}},
  volume       = {{56}},
  year         = {{2020}},
}