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.
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.
the Creative Commons Attribution 4.0 License.
Original research article
| 
13 Jul 2023
Original research article |
| 13 Jul 2023
| 13 Jul 2023
Mapping land degradation risk due to land susceptibility to dust emission and water erosion
Mahdi Boroughani
CORRESPONDING AUTHOR
Research Center for Geoscience and Social Studies, Hakim Sabzevari
University, Sabzevar, Iran
Fahimeh Mirchooli
Research Center for Geoscience and Social Studies, Hakim Sabzevari
University, Sabzevar, Iran
Lab Expert, Sari agricultural Science and Natural Resources
University, Sari, Iran
Mojtaba Hadavifar
Environmental Sciences Department, Hakim Sabzevari University,
Sabzevar, Iran
Stephanie Fiedler
GEOMAR Helmholtz Centre for Ocean Research Kiel & Faculty of
Mathematics and Natural Sciences, Christian-Albrecht University of Kiel, Kiel,
Germany
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Cited
19 citations as recorded by crossref.
- A multi-temporal sandy desert and sandy land classification dataset for the Mongolian Plateau from 1990 to 2020 T. Han et al.
- An interpretable (explainable) model based on machine learning and SHAP interpretation technique for mapping wind erosion hazard H. Gholami et al.
- Mapping dust susceptibility using metaheuristic-optimized light gradient boosting machine (LGBM) M. Shirali et al.
- Groundwater level management in a reclamation system K. Kolesnichenko et al.
- Preparing a map of the sensitivity of the lands of Ilam province to dust production using data mining models S. Pourhashemi
- Land degradation vulnerability mapping in the Middle Omo-Gibe Basin of Ethiopia using MEDALUS model and geospatial techniques A. Dofee et al.
- Introducing the STOP-SaltWind framework enhanced by deep neural networks to investigate aerosol dispersion in Lake Urmia Basin A. Azizi et al.
- The effect of land-use forecasting on dust source susceptibility mapping in Iran S. Pourhashemi et al.
- Multi-hazard susceptibility mapping in the Salt Lake watershed S. Pourhashemi et al.
- Integrating Traditional and Artificial Intelligence Methods in Dust Aerosol Research: A Comprehensive Review T. Sha et al.
- An assessment of global land susceptibility to wind erosion based on deep-active learning modelling and interpretation techniques H. Gholami et al.
- Linking sand/dust storms hotspots and land use over Iran M. Boroughani et al.
- Optimization of stochastic ANN models for intelligent flow-based prediction of soil erosion susceptibility maps (SESM) R. Adnan Ikram et al.
- Development of a composite index for assessing the risk of land degradation Y. Gao et al.
- Species distribution modeling of Malva neglecta Wallr. weed using ten different machine learning algorithms: An approach to site-specific weed management (SSWM) E. Dastres et al.
- Spatiotemporal Dynamics and Driver Pathways of Soil Erosion in Qilian Mountain National Park (1990–2022) Under Ecological Restoration X. Liu et al.
- A comprehensive review of soil erosion research in Central Asian countries (1993-2022) based on the Scopus database M. Juliev et al.
- Assessing transboundary atmospheric pattern changes and local to national air quality impacts of regional fine dust by using novel NASA satellite data a case study of Sarpol e Zahab Iran S. Bahrami Jaf et al.
- Linking dust source susceptibility mapping and land use change in Middle East M. Boroughani et al.
19 citations as recorded by crossref.
- A multi-temporal sandy desert and sandy land classification dataset for the Mongolian Plateau from 1990 to 2020 T. Han et al.
- An interpretable (explainable) model based on machine learning and SHAP interpretation technique for mapping wind erosion hazard H. Gholami et al.
- Mapping dust susceptibility using metaheuristic-optimized light gradient boosting machine (LGBM) M. Shirali et al.
- Groundwater level management in a reclamation system K. Kolesnichenko et al.
- Preparing a map of the sensitivity of the lands of Ilam province to dust production using data mining models S. Pourhashemi
- Land degradation vulnerability mapping in the Middle Omo-Gibe Basin of Ethiopia using MEDALUS model and geospatial techniques A. Dofee et al.
- Introducing the STOP-SaltWind framework enhanced by deep neural networks to investigate aerosol dispersion in Lake Urmia Basin A. Azizi et al.
- The effect of land-use forecasting on dust source susceptibility mapping in Iran S. Pourhashemi et al.
- Multi-hazard susceptibility mapping in the Salt Lake watershed S. Pourhashemi et al.
- Integrating Traditional and Artificial Intelligence Methods in Dust Aerosol Research: A Comprehensive Review T. Sha et al.
- An assessment of global land susceptibility to wind erosion based on deep-active learning modelling and interpretation techniques H. Gholami et al.
- Linking sand/dust storms hotspots and land use over Iran M. Boroughani et al.
- Optimization of stochastic ANN models for intelligent flow-based prediction of soil erosion susceptibility maps (SESM) R. Adnan Ikram et al.
- Development of a composite index for assessing the risk of land degradation Y. Gao et al.
- Species distribution modeling of Malva neglecta Wallr. weed using ten different machine learning algorithms: An approach to site-specific weed management (SSWM) E. Dastres et al.
- Spatiotemporal Dynamics and Driver Pathways of Soil Erosion in Qilian Mountain National Park (1990–2022) Under Ecological Restoration X. Liu et al.
- A comprehensive review of soil erosion research in Central Asian countries (1993-2022) based on the Scopus database M. Juliev et al.
- Assessing transboundary atmospheric pattern changes and local to national air quality impacts of regional fine dust by using novel NASA satellite data a case study of Sarpol e Zahab Iran S. Bahrami Jaf et al.
- Linking dust source susceptibility mapping and land use change in Middle East M. Boroughani et al.
Saved (final revised paper)
Latest update: 28 Apr 2026
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.
The present study used several different datasets, conducted a field survey, and paired the data...
