LAUSR

Abstract Soil loss is a major cause of land degradation worldwide, especially in large areas of arid and semi-arid regions. With advent of new software and technologies such as remote sensing (RS) and GIS, there is a necessity to integrate them to achieve important information in a faster manner. The aims of present study were to evaluate soil erodibility (K-factor) using standard plots under natural rainfall and prediction of soil loss by integrating RUSLE, GIS and RS in Fars Iran. The RUSLE factors were evaluated as following: the R-factor was calculated using modified Fournier index; K-factor was measured in the field using erosion plots and estimated by the USLE equation; the C-factor map was created using the NDVI; the LS-factor map was generated from digital elevation model with 10 m resolution, and the P-factor map was assumed as 1. Spatial distribution of annual soil loss in the Simakan watershed was obtained by multiplying these factors in GIS. The average of the measured K was 0.014 th MJ− 1 mm− 1 and 2.08 times less than the average of the estimated K (0.030 th MJ− 1 mm− 1). The performance of RUSLE was highly influenced by the K, because the annual soil loss predicted using estimated K (11.0 th− 1 ya− 1) was about twice as much as the measured K (5.7 th− 1 ya− 1). The spatial distribution of soil loss classes predicted was: 73.64% very low, 14.79% low, 10.19% moderate and 1.25% severe. Areas of severe soil loss are situated in the northern portion of the study area, which needs suitable conservation practices.