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Photo of Amir Naghibi

Amir Naghibi

Researcher

Photo of Amir Naghibi

Dust source susceptibility in the lower Mesopotamian floodplain of Iraq

Author

  • Ali Al-Hemoud
  • Amir Naghibi
  • Hossein Hashemi
  • Peter Petrov
  • Hebah Kamal
  • Abdulaziz Al-Senafi
  • Ahmed Abdulhadi
  • Megha Thomas
  • Ali Al-Dousari
  • Ghadeer Al-Qadeeri
  • Sarhan Al-Khafaji
  • Vassil Mihalkov
  • Ronny Berndtsson
  • Masoud Soleimani
  • Ali Darvishi Boloorani
Show all

Summary, in English

The identification of susceptible dust sources (SDSs) based on the analysis of effective factors (i.e. dust drivers) is considered to be one of the primary and cost-effective solutions to deal with this phenomenon. Accordingly, this study aimed to identify SDSs and delineate their drivers using remote sensing data and machine learning (ML) algorithms in a hotspot area in the Lower Mesopotamian floodplain in southern Iraq. To model SDSs, a total of 15 environmental features based on remote sensing data such as topographic, climatic, land use/cover, and soil properties were considered as dust drivers and fed into the four well-known ML algorithms, including linear discriminant analysis (LDA), logistic model tree (LMT), extreme gradient boosting (XGB)-Linear, and XGB-Tree-based. Dust emission hotspots were identified by visual interpretation of sub-daily MODIS-Terra/Aqua true color composite imagery (2000–2021) to train (70%) and validate (30%) ML algorithms. Considering the variability of the spatial-temporal patterns of SDSs as a result of changes in dust drivers, the modeling process was carried out in four periods, including 2000–2004, 2005–2007, 2008–2012, and 2013–2021. Our results show that dust events in the study area occur most frequently in April, June, July, and August. Overall, all ML algorithms performed well and provided reliable results for identifying SDSs. However, the XGB-Linear provided the most reliable results with an average area under curve (AUC) of 0.79 for the study periods. Precipitation was determined as the most important dust driver. The SDS maps produced can be used as a basis for the development of rehabilitation plans in the study area to mitigate the adverse effects of dust storms.

Department/s

  • Division of Water Resources Engineering
  • LTH Profile Area: Water
  • Centre for Advanced Middle Eastern Studies (CMES)
  • MECW: The Middle East in the Contemporary World

Publishing year

2024-11

Language

English

Publication/Series

Remote Sensing Applications: Society and Environment

Volume

36

Document type

Journal article

Publisher

Elsevier

Topic

  • Earth Observation

Keywords

  • Dust source susceptibility mapping
  • Dust storm
  • Machine learning
  • Mesopotamian marshes
  • Remote sensing

Status

Published

ISBN/ISSN/Other

  • ISSN: 2352-9385