Assessment of soil erosion patterns using RUSLE model and GIS tools (case study: the border of Khuzestan and Chaharmahal Province, Iran)

Abstract

Soil erosion is one of the most prevailing forms of land degradation all over the world. The type and density of vegetation cover are effective on magnitude of soil erosion. The understanding of interactions between land use management, land cover, and topographical properties of landscape is important to effectively control soil erosion implementing best management practices. Regarding the influences of crop cover characteristics on soil erosion patterns; the presented study was conducted with the aim of evaluation of temporal soil erosion patterns affected by vegetation cover using revised universal soil loss equation (RUSLE) model. The study area is located in the border of Khuzestan and Chaharmahal Province, Iran, country with the geographical coordination of 31° 33′ 48′′ to 32° 25′ 40′′ N and 49° 50′ 44′′ to 50° 51′ 55′′ E. The input factors of RUSLE model including R, K, LS, and CP were generated using the Map algebra in ArcGIS 10.4 software. Afterwards, based on the real data, the 5 and 10 year forecasted of NDVI (normalized difference vegetation cover) was calculated and C-factor was estimated. Moreover, the spatial distribution of C-factor, canopy cover percentage (real and forecasted), Excess Greenness Index, Fractional Vegetation Index and NDVI* were mapped. Our results revealed that areas with the highest NDVI have the lowest C-factor with the lowest water erosion potential (R2 0.998). Also there was no remarkable difference between 5- and 10-year-forecasted C-factor in all studied stations because of no meaningful variations in the rainfall amount during this 5 years. Based on the Excess Greenness Index, the study area mostly is bared with a potential for soil erosion. The map of soil erosion distribution generated by RUSLE model depicted that the average amount of soil erosion was 13.68 tone/ha/year (STD 22.28), whereas for 10-year forecasted of soil erosion was 6.42 tone/ha/year (STD 9.80). Our mapping results illustrated that there was three main classes of soil erosion patterns including, the area with erosion mitigation, the area without change, and the area with increasing of soil erosion. These patterns confirmed that the vegetation cover distribution was different in the study area. Regarding the reduction of R-factor, the 10-year-forecasted soil erosion meaningfully decreased. Generally, the RUSLE model coupled with ArcGIS was capable to simulate soil erosion patterns and generate the spatial distribution map of erosion which is convenient visual tool to monitor and control of soil erosion. Furthermore, due to the variability of C-factor (temporal and spatial), therefore, more field experiments will be conducted for validating the C-factor forecasting using vegetation indices.