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

Abstract

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 ((Formula presented.)), crop management ((Formula presented.)), rainfall erosivity ((Formula presented.)), soil erodibility ((Formula presented.)), and support practice ((Formula presented.)) range from 0–68,227, 0–66.61%, 0–0.58, 495.99–648.68 (Formula presented.) 0.15–0.25 (Formula presented.), and 1 respectively. The results indicate that the estimated total annual potential soil loss of approximately 4,67,064.25 (Formula presented.) is comparable with the measured sediment loss of 11,631 (Formula presented.) during the water year 2020. The predicted soil erosion rate due to an increase in agricultural area is approximately 164,249.31 (Formula presented.). 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 (Formula presented.). 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.