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Integration of RUSLE Model, Remote Sensing and GIS Techniques for Assessing Soil Erosion Hazards in Arid Zones

Author

Listed:
  • Elsayed A. Abdelsamie

    (National Authority for Remote Sensing and Space Sciences, Cairo 11843, Egypt)

  • Mostafa A. Abdellatif

    (National Authority for Remote Sensing and Space Sciences, Cairo 11843, Egypt)

  • Farag O. Hassan

    (National Authority for Remote Sensing and Space Sciences, Cairo 11843, Egypt)

  • Ahmed A. El Baroudy

    (Soil and Water Department, Faculty of Agriculture, Tanta University, Tanta 31527, Egypt)

  • Elsayed Said Mohamed

    (National Authority for Remote Sensing and Space Sciences, Cairo 11843, Egypt
    Department of Environmental Management, Institute of Environmental Engineering, People’s Friendship University of Russia (RUDN University), 6 Miklukho-Maklaya Street, 117198 Moscow, Russia)

  • Dmitry E. Kucher

    (Department of Environmental Management, Institute of Environmental Engineering, People’s Friendship University of Russia (RUDN University), 6 Miklukho-Maklaya Street, 117198 Moscow, Russia)

  • Mohamed S. Shokr

    (Soil and Water Department, Faculty of Agriculture, Tanta University, Tanta 31527, Egypt)

Abstract

Soil erosion constitutes one of the main environmental and food security threats, derived from the loss of its productive capacity. With the help of remote sensing (RS), geographic information systems (GIS), and a revised version of the universal soil loss equation (RUSLE), this research has mostly focused on measuring the potential soil erosion hazard and soil water conservation ratio (SWCR) in the El-Minia region of Egypt. Based on the integration of S2A images and the digital elevation model (DEM), geomorphological units of the study area were identified. The RUSLE model includes parameters that allow for mapping soil erosion, such as rain erosivity, soil erodibility, slope length and steepness, soil cover and management, and soil conservation practices. The outcomes revealed that the classes of annual erosion rates of the study area are those of “slight erosion”, “low erosion”, “moderate erosion” and “moderately high erosion”, which represent percentages of 29%, 18%, 33% and 20%, respectively, of the total area. The rate of erosion decreases from east to west. The main erosion factors in the research area are the low vegetation cover and the high slope values. This study highlights the utility of combining the classic RUSLE equation with techniques such as remote sensing (RS) and geographic information systems (GIS) as a basis for assessing current erosion conditions in arid environments and, specifically, for the application of soil management patterns aimed at increasing soil organic matter and any other soil conservation actions. The findings of this study can be used by policymakers to implement soil conservation measures if development projects are to proceed in areas with a high risk of soil erosion. The approach described here is therefore adaptable to similar environments in arid regions.

Suggested Citation

  • Elsayed A. Abdelsamie & Mostafa A. Abdellatif & Farag O. Hassan & Ahmed A. El Baroudy & Elsayed Said Mohamed & Dmitry E. Kucher & Mohamed S. Shokr, 2022. "Integration of RUSLE Model, Remote Sensing and GIS Techniques for Assessing Soil Erosion Hazards in Arid Zones," Agriculture, MDPI, vol. 13(1), pages 1-19, December.
    • Handle: RePEc:gam:jagris:v:13:y:2022:i:1:p:35-:d:1012014
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    References listed on IDEAS

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    1. Jinghu Pan & Yan Wen, 2014. "Estimation of soil erosion using RUSLE in Caijiamiao watershed, China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 71(3), pages 2187-2205, April.
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    4. Mostafa A. Abdellatif & Ahmed A. El Baroudy & Muhammad Arshad & Esawy K. Mahmoud & Ahmed M. Saleh & Farahat S. Moghanm & Kamal H. Shaltout & Ebrahem M. Eid & Mohamed S. Shokr, 2021. "A GIS-Based Approach for the Quantitative Assessment of Soil Quality and Sustainable Agriculture," Sustainability, MDPI, vol. 13(23), pages 1-24, December.
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    1. Hamza Bouguerra & Salah Eddine Tachi & Hamza Bouchehed & Gordon Gilja & Nadir Aloui & Yacine Hasnaoui & Abdelmalek Aliche & Saâdia Benmamar & Jose Navarro-Pedreño, 2023. "Integration of High-Accuracy Geospatial Data and Machine Learning Approaches for Soil Erosion Susceptibility Mapping in the Mediterranean Region: A Case Study of the Macta Basin, Algeria," Sustainability, MDPI, vol. 15(13), pages 1-23, June.

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