Digital soil maps at proper scales can support sustainable land-use planning over large areas; however, applying
this approach in erosion studies is still limited. Therefore, in this work, we adopted remote sensing-based
mapping and fuzzy logic techniques to develop wind erosion risk maps with a spatial resolution of 30 m.
These procedures were then conducted in a newly-reclaimed area in Matruh Governorate, the Egyptian western
desert. Sixty surface soil samples (0–30 cm) were collected and analyzed for soil erodibility-related properties
(sand, silt, clay, organic matter, and CaCO3). The relationships of these properties with the reflectance data of
Landsat 8 multispectral bands were explored. Then, the spatial predictions were performed using the stepwise
multiple linear regression models. The results showed that band reflectance in the shortwave infrared spectrum
had higher correlations with soil properties than in the visible and near-infrared regions. The regression models
achieved reliable precision and acceptable prediction abilities for all soil properties, except for the silt content.
For validation datasets (30 % of the total data), the best-fitted models had coeffect of determination (R2) and
residual prediction deviation of 0.61–0.92 and 1.42–2.67, respectively. The fuzzy memberships revealed various
contributions of the five drivers (climate erosivity, soil erodibility, soil crust, vegetation, and terrain roughness)
to potential soil loss. The success rate curves indicated the different performances of the applied fuzzy overlay
operators (AND, OR, Sum, Product, and Gamma 0.9). The best-predictive model with an overall success rate of
85.7 % was obtained by overlaying the five fuzzified layers under the fuzzy algebraic Sum operator. This model
delineated five hazard classes over the study area, including severe (79.5 %), high (12.6 %), moderate (5.7 %),
slight (1.8 %), and very slight (0.4 %). The developed approach would improve the insight into large-scale
monitoring of wind erosion status in desert ecosystems. |
