Soil Loss Estimation Using GIS and Remote Sensing Technique: A Case of Teaching and Research Farm, Kwara State University, Malete

Abstract

Due to ongoing cultivation, the Teaching and Research Farm of Kwara State University in Malete has seen quick and accelerated erosion. Run-off-related soil loss is a significant and persistent ecological problem in the study area. Information on soil loss is essential for promoting agricultural production and natural resource management. The average annual soil loss was calculated and mapped in this study using remote sensing and GIS. The soil loss was computed using the Revised Universal Soil Loss (RUSLE) Model. Using a topographic map at a scale of 1:50,000, an aster digital elevation model (DEM) with a spatial resolution of 20 m, a digital soil map at a scale of 1: 250,000, rainfall data spanning 39 years (1981-2020), and other data, RUSLE's soil loss variables were calculated. The RUSLE parameters were investigated and incorporated using a raster calculator in the geoprocessing tools in the arc-GIS 10.1 environment to estimate and map the annual soil loss of the research region. The results show that the annual soil loss in the study region ranged from 48.553 to 1,476.606 t ha-1 year-1 , cover around 100 ha of land. Most of the soil erosion affected areas are spatially situated in block 2B and 3A part of the farm. These are areas where low Ferric Luvisols and high Ferric Luvisols with higher soil erodibility character (21- 33) values are dominant. Therefore, it was found that the main causes of soil erosion were slope gradient and length, followed by soil erodibility parameters. The study therefore recommended using sustainable soil and water conservation methods to address the problem of soil erosion in the study area.

Keywords

• soil loss
• soil erodibility
• slope gradient
• arc-gis
• ferric luvisols
• rusle