Soil Loss Estimation Using Remote Sensing and RUSLE Model in Koromi-Federe Catchment Area of Jos-East LGA, Plateau State, Nigeria

Abstract

Soil loss caused by erosion has destroyed landscapes, as well as depositing sterile material on fertile lands and rivers, clogged waterways and accelerated flash floods, declined the populations of fish and other species, and diminish soil fertility. In some places, erosion has also destroyed buildings, caused mudflow, create new landforms, displaced people, and slowed down the economy of the affected community by destroying roads and homes. Erosion is aggravated by climate change and anthropogenic factors such as deforestation, overgrazing, inappropriate methods of tillage, and unsustainable agricultural practices. In this study, remote sensing (RS) and geographic information (GIS) data and tools were used to model erosion and estimate soil loss in the catchment area of Koromi-Federe in Jos East, Plateau State Nigeria which is our study area. Soil loss estimation was performed using the revised universal soil loss equation (RUSLE) model and was computed by substituting the corresponding values of each factor inherent in the equation (rainfall erosivity, soil erodibility, slope steepness and slope length, cover management, and conservation practices) using RS and GIS tools. Soil data was obtained from the study area and analyzed in the laboratory, rainfall data, land cover, digital elevation model (DEM), as well as the management practice of the study area were the parameters computed in spatial analyst tool using map algebra based on RUSLE. The soil loss generated was classified into four classes and the results revealed 95.27% of the catchment with a tolerable loss of less than 10 t/h−1/y−1. At 3.6%, a low or minimal loss of 10–20 t/h−1/y−1, at 1.03% there exists a moderate loss of 20–50 t/h−1/y−1, while there was and critical or high loss of >50 t/h−1/y−1 at 0.12% of the catchment. The result showed that critical soil loss in the catchment area is exacerbated by the influence of the slope length and steepness, and the amount of rainfall received. This poses great concern with annual rainfall projected to increase up to 12% in West Africa. However, our sensitivity analysis revealed that it can be reduced with the effect of vegetated cover and management practices. This is an important finding as it can guide sustainability practices to control erosion and the loss of valuable lands in the region, especially now under climate change.

A review of soil erosion modeling in Nigeria using the Revised Universal Soil Loss Equation model

“…Tolerable soil loss of 0–10 t ha −1 year −1 over a greater part of the Koromi‐Federe catchment in Jos East of Plateau State was reported by Ugese et al. (2022). In the Obibia watershed, soil loss of 0–543 t ha −1 year −1 was obtained (Okenmuo & Ewemoje, 2023).…”

“…They show that soil loss was higher on the bare surface but decreased toward the forested area (Olorunfemi et al, 2020). Tolerable soil loss of 0-10 t ha −1 year −1 over a greater part of the Koromi-Federe catchment in Jos East of Plateau State was reported by Ugese et al (2022). In the Obibia watershed, soil loss of 0-543 t ha −1 year −1 was obtained (Okenmuo & Ewemoje, 2023).…”

Impact of forest landscape restoration in combating soil erosion in the Lake Abaya catchment, Southern Ethiopia

“…The resistance of the soil against erosion as a result of rain interception and the rate and amount of runoff produced for the rainfall impact usually depends on geological and soil features (Hadas, 1994 ; Ugese et al, 2022 ). The equation by Wawer et al ( 2005 ) was applied with data on soil mass as a percentage of sand, silt, clay, and organic carbon for this study.…”

Soil erosion assessment and identification of erosion hotspot areas in the upper Tekeze Basin, Northern Ethiopia

“…RUSLE is used to estimate annual average soil loss rate by accounting for rainfall erosivity, soil erodibility, slope, land use, and land management attributes [ 38 , 42 ]. Since soil erosion assessment as well as sustainable land and water management are increasingly requiring big-data-driven policymaking and management strategies; Remote sensing (RS) and Geographic Information System (GIS) tools are facilitating the development of soil erosion models [ [45] , [46] , [47] ]. However, RS and GIS based soil erosion models require extensive field soil data collection, which is expensive and time-consuming constraining soil erosion assessment for present priority use.…”