Soil Erodibility and its Spatial Variation in Areas under Erosion Control Measures in Behbahan Region

To control soil erosion, several measures can be conducted which information on their effects is very important in managing soil erosion. Therefore, this study was conducted to assess and model soil erodibility in two adjacent sites in Behbahan region (Khuzestan province). At one site afforestation and contour furrowing were conducted to control soil erosion and the other site without any controlling measures was considered as control. Totally 150 soil samples were collected from the surface layer (0-5 cm) and K was estimated using the methods introduced by Wischmeier and Smith (method A) and Vaezi et al. (method B). For spatial modelling of K, based on digital soil mapping (DSM) techniques, several environmental covariates were derived from a Landsat 8 image and a digital elevation model (DEM) and two models including random forests (RF) and artificial neural networks (ANN) were employed. The values of K for methods A and B varied from 0.025 to 0.087 and 0.002 to 0.008 t.ha.h/ha.Mj.mm with means of 0.067 and 0.006 t.ha.h/ha.Mj.mm, respectively. Results revealed good correlation between K and remotely sensed covariates. Although K (method B) had significant correlation with some of the covariates derived from DEM, but there was no significant correlation between K (method A) with all covariates derived from DEM. Results indicated a significant difference between two sites it terms of K estimated by method A, while there was no significant difference in case of K estimated by method B. Model validation showed that both RF and ANN models resulted in good and unbiased estimates of K (methods A and B). In general, the findings indicated, although the performance of DSM techniques in modeling K were high, performances of the models and the results of means compassion may significantly differ in terms of the method through which K is estimated.