Assessing Land Degradation Through Remote Sensing and Geospatial Techniques for Sustainable Development Under the Mediterranean Conditions

Elsherbiny A. Ali, Ahmed S. Elnagar, Nazih Y. Rebouh () and Mohamed E. Fadl ()
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Elsherbiny A. Ali: Geography Department, Faculty of Arts, Zagazig University, Zagazig 44519, Egypt
Ahmed S. Elnagar: Geography Department, Faculty of Arts, Zagazig University, Zagazig 44519, Egypt
Nazih Y. Rebouh: Department of Environmental Management, Institute of Environmental Engineering, RUDN University, 6 Miklukho-Maklaya St., Moscow 117198, Russia
Mohamed E. Fadl: Division of Scientific Training and Continuous Studies, National Authority for Remote Sensing and Space Sciences (NARSS), Cairo 11769, Egypt

Sustainability, 2025, vol. 17, issue 13, 1-28

Abstract: This study provides a comprehensive assessment of land degradation (LD) in Damietta Governorate, Egypt, by integrating multiple indices, including the Geology Index (GI), Topographic Quality Index (TQI), Physical Quality Index (PQI), Chemical Quality Index (CQI), Wind Erosion Quality Index (WEQI), and Vegetation Quality Index (VQI). The study findings reveal the following: (1) Soil quality shows moderate suitability for agricultural and developmental activities and can support productive land use with proper management (68.14% physical quality, 51.54% chemical quality), with 14.03–37.75% high-quality areas supporting intensive farming and 10.71–17.83% degraded soils requiring intervention; (2) nearly 31.83% of the area faces high degradation risk, particularly from wind erosion (27.41% high-risk areas), emphasizing the need for erosion control measures; and (3) vegetation analysis shows that 51.5% of land has inadequate cover (low/very low quality), highlighting restoration needs. The LD mapping reveals that 32.70% of the area is at low risk, 35.48% at moderate risk, and 31.83% at high to very high risk, underscoring the need for urgent restoration and sustainable land management practices. The study validates the effectiveness of ordinary kriging (OK) models in predicting soil properties, with tailored variogram models (Exponential, Spherical, and Gaussian) enhancing prediction accuracy. Overall, this study identifies statistically significant factors influencing LD in the study area, providing a data-driven foundation for sustainable land management, agricultural development, and environmental conservation.

Keywords: land degradation; geospatial techniques; soil quality indices; sustainable land management; quality Indices (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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