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Articles | Volume 9, issue 2
Articles | Volume 9, issue 2
https://doi.org/10.5194/soil-9-411-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/soil-9-411-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 9, issue 2
Original research article
 | 
13 Jul 2023
Original research article |  | 13 Jul 2023

Mapping land degradation risk due to land susceptibility to dust emission and water erosion

Mahdi Boroughani, Fahimeh Mirchooli, Mojtaba Hadavifar, and Stephanie Fiedler

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Short summary
The present study used several different datasets, conducted a field survey, and paired the data with three different machine learning algorithms to construct spatial maps for areas at risk of land degradation for the Lut watershed in Iran. According to the land degradation map, almost the entire study region is at risk. A large fraction of 43 % of the area is prone to both high wind-driven and water-driven soil erosion.
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