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.

Novel spatial models for appraising arable land resources using data processing techniques can increase insight into agroecosystem services. Hence, the principal component analysis (PCA), hierarchal cluster analysis (HCA), analytical hierarchy process (AHP), fuzzy logic, and geographic information system (GIS) were integrated to zone and map agricultural land quality in an arid desert area (Matrouh Governorate, Egypt). Satellite imageries, field surveys, and soil analyses were employed to define eighteen indicators for terrain, soil, and vegetation qualities, which were then reduced through PCA to a minimum data set (MDS). The original and MDS were weighted by AHP through experts’ opinions. Within GIS, the raster layers were generated, standardized using fuzzy membership functions (linear and non-linear), and assembled using arithmetic mean and weighted sum algorithms to produce eight land quality index maps. The soil properties (pH, salinity, organic matter, and sand), slope, surface roughness, and vegetation could adequately express the land quality. Accordingly, the HCA could classify the area into eight spatial zones with significant heterogeneity. Selecting salt-tolerant crops, applying leaching fraction, adopting sulfur and organic applications, performing land leveling, and using micro-irrigation are the most recommended practices. Highly significant (p < 0.01) positive correlations occurred among all the developed indices. Nevertheless, the coefficient of variation (CV) and sensitivity index (SI) confirmed the better performance of the index developed from the non-linearly scored MDS and weighted sum model. It could achieve the highest discrimination in land qualities (CV > 35%) and was the most sensitive (SI = 3.88) to potential changes. The MDS within this index could sufficiently represent TDS (R² = 0.88 and Kappa statistics = 0.62), reducing time, effort, and cost for estimating the land performance. The proposed approach would provide guidelines for sustainable land-use planning in the studied area and similar regions.