Soil erosion assessment by RUSLE model using remote sensing and GIS in an arid zone

In this research, we used the Revised Universal Soil Loss Equation (RUSLE) and Geographical Information System (GIS) to predict the annual rate of soil loss in the District Chakwal of Pakistan. The parameters of the RUSLE model were estimated using remote sensing data, and the erosion probability zones were determined using GIS. The estimated length slope (LS), crop management (C), rainfall erosivity (R), soil erodibility (K), and support practice (P) range from 0-68,227, 0-66.61%, 0-0.58, 495.99-648.68 MJ/mm.t.ha(-1) .year(-1), 0.15-0.25 MJ/mm.t.ha(-1) .year(-1), and 1 respectively. The results indicate that the estimated total annual potential soil loss of approximately 4,67,064.25 t.ha(-1).year(-1) is comparable with the measured sediment loss of 11,631 t.ha(-1).year(-1) during the water year 2020. The predicted soil erosion rate due to an increase in agricultural area is approximately 164,249.31 t.ha(-1).year(-1). In this study, we also used Landsat imagery to rapidly achieve actual land use classification. Meanwhile, 38.13% of the region was threatened by very high soil erosion, where the quantity of soil erosion ranged from 365487.35 t.ha(-1).year(-1). Integrating GIS and remote sensing with the RUSLE model helped researchers achieve their final objectives. Land-use planners and decision-makers use the result's spatial distribution of soil erosion in District Chakwal for conservation and management planning.

Automated summary

In this study, the Revised Universal Soil Loss Equation (RUSLE) and Geographical Information System (GIS) were used to predict the annual rate of soil loss in District Chakwal, Pakistan. The parameters of the RUSLE model were estimated using remote sensing data, and GIS was used to determine erosion probability zones. The results show that the estimated total annual potential soil loss is comparable to the measured sediment loss, and the predicted soil erosion rate due to an increase in agricultural area is also significant. Integrating GIS and remote sensing with the RUSLE model helped achieve the objectives of the study.