268
Views
2
CrossRef citations to date
0
Altmetric
Research Articles

Identification of dust sources in a dust hot-spot area in Iran using multi-spectral Sentinel 2 data and deep learning artificial intelligence machine

, , & ORCID Icon
Pages 10950-10969 | Received 31 Aug 2021, Accepted 12 Feb 2022, Published online: 27 May 2022
 

Abstract

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.

Acknowledgements

Mojtaba Dolatkordestani as a postdoctoral researcher has been funded to work at Shahid Beheshti University by the Iran National Science Foundation (INSF) (grant number 98012293).

Disclosure statement

No potential conflict of interest was reported by the authors.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access
  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart
* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.