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

• Published in: Soil 2023-07, Vol.9 (2), p.411-423
• Main Authors: Boroughani, Mahdi, Mirchooli, Fahimeh, Hadavifar, Mojtaba, Fiedler, Stephanie
• Format: Article
• Language: English
• Subjects: Aerosols; Algorithms; Artificial neural networks; Atmospheric particulates; Degradation; Discriminant analysis; Dust; Dust storms; Environmental degradation; Environmental impact; Eolian soils; Global positioning systems; GPS; Land degradation; Learning algorithms; Machine learning; Neural networks; Remote sensing; Risk; Satellite imagery; Satellites; Soil degradation; Soil erosion; Soil water storage; Water; Water erosion; Watershed management; Watersheds; Wind erosion
• ISSN: 2199-398X; 2199-3971; 2199-398X; 2199-3971
• DOI: 10.5194/soil-9-411-2023

Land degradation is a cause of many social, economic, and environmental problems. Therefore identification and monitoring of high-risk areas for land degradation are necessary. Despite the importance of land degradation due to wind and water erosion in some areas of the world, the combined study of both types of erosion in the same area receives relatively little attention. The present study aims to create a land degradation map in terms of soil erosion caused by wind and water erosion of semi-dry land. We focus on the Lut watershed in Iran, encompassing the Lut Desert that is influenced by both monsoon rainfalls and dust storms. Dust sources are identified using MODIS satellite images with the help of four different indices to quantify uncertainty. The dust source maps are assessed with three machine learning algorithms encompassing the artificial neural network (ANN), random forest (RF), and flexible discriminant analysis (FDA) to map dust sources paired with soil erosion susceptibility due to water. We assess the accuracy of the maps from the machine learning results with the area under the curve (AUC) of the receiver operating characteristic (ROC) metric. The water and aeolian soil erosion maps are used to identify different classes of land degradation risks. The results show that 43 % of the watershed is prone to land degradation in terms of both aeolian and water erosion. Most regions (45 %) have a risk of water erosion and some regions (7 %) a risk of aeolian erosion. Only a small fraction (4 %) of the total area of the region had a low to very low susceptibility for land degradation. The results of this study underline the risk of land degradation for in an inhabited region in Iran. Future work should focus on land degradation associated with soil erosion from water and storms in larger regions to evaluate the risks also elsewhere.