Understanding the change dynamics of land use and land cover (LULC) is critical for efficient ecological management modification and sustainable land-use planning. This work aimed to identify, simulate, and predict historical and future LULC changes in the Sohag Governorate, Egypt, as an arid region. In the present study, the detection of historical LULC change dynamics for time series 1984–2002, 2002–2013, and 2013–2022 was performed, as well as CA-Markov hybrid model was employed to project the future LULC trends for 2030, 2040, and 2050. Four Landsat images acquired by different sensors were used as spatial–temporal data sources for the study region, including TM for 1984, ETM+ for 2002, and OLI for 2013 and 2022. Furthermore, a supervised classification technique was implemented in the image classification process. All remote sensing data was processed and modeled using IDRISI 7.02 software. Four main LULC categories were recognized in the study region: urban areas, cultivated lands, desert lands, and water bodies. The precision of LULC categorization analysis was high, with Kappa coefficients above 0.7 and overall accuracy above 87.5% for all classifications. The results obtained from estimating LULC change in the period from 1984 to 2022 indicated that built-up areas expanded to cover 12.5% of the study area in 2022 instead of 5.5% in 1984. This urban sprawl occurred at the cost of reducing old farmlands in old towns and villages and building new settlements on bare lands. Furthermore, cultivated lands increased from 45.5% of the total area in 1984 to 60.7% in 2022 due to ongoing soil reclamation projects in desert areas outside the Nile Valley. Moreover, between 1984 and 2022, desert lands lost around half of their area, while water bodies gained a very slight increase. According to the simulation and projection of the future LULC trends for 2030, 2040, and 2050, similar trends to historical LULC changes were detected. These trends are represented by decreasing desert lands and increasing urban and cultivated newly reclaimed areas. Concerning CA-Markov model validation, Kappa indices ranged across actual and simulated maps from 0.84 to 0.93, suggesting that this model was reasonably excellent at projecting future LULC trends. Therefore, using the CA-Markov hybrid model as a prediction and modeling approach for future LULC trends provides a good vision for monitoring and reducing the negative impacts of LULC changes, supporting land use policy-makers, and developing land management.

Modelling land degradation vulnerability (LDV) in the newly-reclaimed desert oases is a key factor for sustainable agricultural production. In the present work, a trial for using remote sensing data, GIS tools, and Analytic Hierarchy Process (AHP) was conducted for modeling and evaluating LDV. The model was then applied within 144,566 ha in Farafra, an inland hyper-arid Western Desert Oases in Egypt. Data collected from climate conditions, geological maps, remote sensing imageries, field observations, and laboratory analyses were conducted and subjected to AHP to develop six indices. They included geology index (GI), topographic quality index (TQI), physical soil quality index (PSQI), chemical soil quality index (CSQI), wind erosion quality index (WEQI), and vegetation quality index (VQI). Weights derived from the AHP showed that the effective drivers of LDV in the studied area were as follows: CSQI (0.30) > PSQI (0.29) > VQI (0.17) > TQI (0.12) > GI (0.07) > WEQI (0.05). The LDV map indicated that nearly 85% of the total area was prone to moderate degradation risks, 11% was prone to high risks, while less than 1% was prone to low risks. The consistency ratio (CR) for all studied parameters and indices were less than 0.1, demonstrating the high accuracy of the AHP. The results of the cross-validation demonstrated that the performance of ordinary kriging models (spherical, exponential, and Gaussian) was suitable and reliable for predicting and mapping soil properties. Integrated use of remote sensing data, GIS, and AHP would provide an effective methodology for predicting LDV in desert oases, by which proper management strategies could be adopted to achieve sustainable food security.

Evaluating soil quality (SQ) is essential to track changes in soil performance in response to management practices. The current work aimed at using remote sensing and GIS to assess SQ in Dakahlia Governorate, north Nile Delta of Egypt. Twenty-one soil profiles representing the main geomorphic units in the area were dug and samples were collected from different horizons and analysed. Twenty-nine parameters were used to calculate 4 soil quality indices; chemical, physical, fertility and environmental. The four indices were assembled with the geomorphic units in four thematic layers, which were overlain in accordance with geometric mean algorism in a single map. The proposed model showed that about 29% of the total agricultural lands were classified as very high, 30% as high, 33% as moderate, 3% as low and 5% as very low quality. Results of the proposed model agreed with those calculated by the additive index in 95% of the cases, while they agreed with the weighted additive index in 71% of the cases. The Kappa statics indicated an almost perfect agreement between the proposed model and the additive model, while a moderate agreement with the weighted additive model. The proposed model had the highest sensitivity index, indicating its accuracy for evaluating SQ in the studied area. The developed model would provide a useful tool for proper monitoring of soil performance in the studied area.

Desertification is a serious threat to human survival and to ecosystems, especially to inland desert oases. An assessment of desertification severity is essential to ensure national sustainable development for agricultural and land expansion processes in this region. In this study, Index of Land Susceptibility to Wind Erosion (ILSWE) was integrated with a Modified Mediterranean Desertification and Land Use (MEDALUS) method and factor analysis (FA) to develop a GIS-based model for mapping desertification severity. The model was then applied to 987.77 km² in the El-Farafra Oasis, located in the Western Desert of Egypt, as a case study. Climate and field survey data together with remote sensing images were used to generate five quality indices (soil, climate, vegetation, land management and wind erosion). Based on the FA, a weighted value was assigned to each index. Five thematic layers representing the indices were created within the GIS environment and overlaid using the weighted sum model. The developed model showed that 59% of the total area was identified as high-critical and 38% as medium-critical. The results of an environmentally sensitive area index suggested by the original MEDALUS model indicated similar results: 18.37% of the total area was classified as high-critical and 78.73% as medium-critical. However, the sensitivity analysis indicated that weights derived from FA resulted in better performance of the developed spatial model than that derived from the original MEDALUS method. The proposed model would be a suitable tool for monitoring vulnerable zones, and could be a starting point for sustainable agricultural development in inland oases.

Oasis lands in Egypt are commonly described as salty soils; therefore, waterlogging and higher soil salinity are major obstacles to sustainable agricultural development. This study aims to map and assess soil salinization at El-Farafra Oasis in the Egypt Western Desert based on salinity indices, Imaging Spectroscopy (IS), and statistical techniques. The regression model was developed to test the relationship between the electrical conductivity (EC_e) of 70 surface soil samples and seven salinity indices (SI 1, SI 2, SI 5, SI 6, SI 7, SI 8, and SI 9) to produce soil salinity maps depending on Landsat-8 (OLI) images. The investigations of soil salinization and salinity indices were validated in a studied area based on 30 soil samples; the obtained results represented that all salinity indices have shown satisfactory correlations between EC_e values for each soil sample site and salinity indices, except for the SI 5 index that present non-significant correlations with R² value of 0.2688. The SI 8 index shows a higher negative significant correlation with EC_e and an R² value of 0.6356. There is a significant positive correlation at the (p < 0.01) level between SI 9 and EC_e (r = 0.514), a non-significant correlation at the (p < 0.05) level between soil EC_e and SI 1 index (r = 0.495), and the best-verified salinity index was for SI 7 that has a low estimated RMSE error of 8.58. Finally, the highest standard error (R²) was represented as EC_e (dS m⁻¹) with an R² of 0.881, and the lowest one was SI 9 with an R² of 0.428, according to Tukey’s test analysis. Therefore, observing and investigating soil salinity are essential requirements for appropriate natural resource management plans in the future.

The reduced availability of water resources in Egypt has imposed the need to intensify the use of wastewater for crop irrigation in the alluvial soils. Relevant effects can derive from contents of potentially toxic metals (PTMs) in supply resources soils, crops, and groundwater in these areas. For this reason the PTM content has to be monitored to evaluate and minimize health hazards. Therefore, in this context, two areas of the SE Nile Delta subjected to 25 year of wastewater irrigation, using agricultural drainage water (ADW) and mixed wastewater (MWW) were chosen and compared with a nearby site irrigated with Nile freshwater (NFW). At each of the three sites, ten samples of irrigation water, topsoil, berseem clover (Trifolium alexandrinum L.) plants, and seven groundwater samples were collected and analyzed for Cr, Co, Cu, Pb, Ni, and Zn. Results indicate that the total contents of Co, Cu, Ni, and Zn in soils collected from the three sampling sites and Pb in the MWW-irrigated soils were higher than their average natural contents in the earth’s crust, indicating potential risks. The DTPA-extractable contents of Cu in the three sites, in addition to Pb and Zn in the MWW-irrigated soils, exceeded the safe limits. The MWW-irrigated soils showed a considerable degree of metal contamination, while the NFW- and ADW-irrigated soils showed moderate and low levels of contamination, respectively. The contents of the six PTMs in the three sites showed low individual ecological risks, except for Pb in the MWW-irrigated soils that showed a moderate risk; however, the overall ecological risk remained low in all samples. The values of Co, Cu, and Ni in berseem shoot in addition to Pb from the MWW-irrigated soils were over the maximum permissible levels for animal feeding. Values of root-to-shoot translocation factor were lower than 1.0 for Cr, Co and Ni but higher than 1.0 for Cu, Pb, and Zn. Berssem plant is a good candidate for phytofiltration of Cr, Co and Ni, while for extracting Cu, Pb and Zn from polluted soils. The groundwater samples collected from the three sampling sites showed lower metal concentrations than the safe limits for drinking standards. Further remediation studies should be taken into account to alleviate potential environmental and health-related risks when using supply resources different from freshwater.